prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What traditional linguistics features did they use?
Context 1: Introduction
Sarcasm is an intensive, indirect and complex construct that is often intended to express contempt or ridicule   Sarcasm, in speech, is multi-modal, involving tone, body-language and gestures along with linguistic artifacts used in speech  Sarcasm in text, on the other hand, is more restrictive when it comes to such non-linguistic modalities  This makes recognizing textual sarcasm more challenging for both humans and machines 
Sarcasm detection plays an indispensable role in applications like online review summarizers, dialog systems, recommendation systems and sentiment analyzers  This makes automatic detection of sarcasm an important problem  However, it has been quite difficult to solve such a problem with traditional NLP tools and techniques  This is apparent from the results reported by the survey from DBLP:journals/corr/JoshiBC16  The following discussion brings more insights into this 
Consider a scenario where an online reviewer gives a negative opinion about a movie through sarcasm: “This is the kind of movie you see because the theater has air conditioning”  It is difficult for an automatic sentiment analyzer to assign a rating to the movie and, in the absence of any other information, such a system may not be able to comprehend that prioritizing the air-conditioning facilities of the theater over the movie experience indicates a negative sentiment towards the movie  This gives an intuition to why, for sarcasm detection, it is necessary to go beyond textual analysis 
We aim to address this problem by exploiting the psycholinguistic side of sarcasm detection, using cognitive features extracted with the help of eye-tracking  A motivation to consider cognitive features comes from analyzing human eye-movement trajectories that supports the conjecture: Reading sarcastic texts induces distinctive eye movement patterns, compared to literal texts  The cognitive features, derived from human eye movement patterns observed during reading, include two primary feature types:
The cognitive features, along with textual features used in best available sarcasm detectors, are used to train binary classifiers against given sarcasm labels  Our experiments show significant improvement in classification accuracy over the state of the art, by performing such augmentation 
Related Work
Sarcasm, in general, has been the focus of research for quite some time  In one of the pioneering works jorgensen1984test explained how sarcasm arises when a figurative meaning is used opposite to the literal meaning of the utterance  In the word of clark1984pretense, sarcasm processing involves canceling the indirectly negated message and replacing it with the implicated one  giora1995irony, on the other hand, define sarcasm as a mode of indirect negation that requires processing of both negated and implicated messages  ivanko2003context define sarcasm as a six tuple entity consisting of a speaker, a listener, Context, Utterance, Literal Proposition and Intended Proposition and study the cognitive aspects of sarcasm processing 
Computational linguists have previously addressed this problem using rule based and statistical techniques, that make use of : (a) Unigrams and Pragmatic features BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 (b) Stylistic patterns BIBREF4 and patterns related to situational disparity BIBREF5 and (c) Hastag interpretations BIBREF6 , BIBREF7  
Most of the previously done work on sarcasm detection uses distant supervision based techniques (ex: leveraging hashtags) and stylistic/pragmatic features (emoticons, laughter expressions such as “lol” etc)  But, detecting sarcasm in linguistically well-formed structures, in absence of explicit cues or information (like emoticons), proves to be hard using such linguistic/stylistic features alone 
With the advent of sophisticated eye-trackers and electro/magneto-encephalographic (EEG/MEG) devices, it has been possible to delve deep into the cognitive underpinnings of sarcasm understanding  Filik2014, using a series of eye-tracking and EEG experiments try to show that for unfamiliar ironies, the literal interpretation would be computed first  They also show that a mismatch with context would lead to a re-interpretation of the statement, as being ironic  Camblin2007103 show that in multi-sentence passages, discourse congruence has robust effects on eye movements  This also implies that disrupted processing occurs for discourse incongruent words, even though they are perfectly congruous at the sentence level  In our previous work BIBREF8 , we augment cognitive features, derived from eye-movement patterns of readers, with textual features to detect whether a human reader has realized the presence of sarcasm in text or not 
The recent advancements in the literature discussed above, motivate us to explore gaze-based cognition for sarcasm detection  As far as we know, our work is the first of its kind 
Eye-tracking Database for Sarcasm Analysis
Sarcasm often emanates from incongruity BIBREF9 , which enforces the brain to reanalyze it BIBREF10   This, in turn, affects the way eyes move through the text  Hence, distinctive eye-movement patterns may be observed in the case of successful processing of sarcasm in text in contrast to literal texts  This hypothesis forms the crux of our method for sarcasm detection and we validate this using our previously released freely available sarcasm dataset BIBREF8 enriched with gaze information 
Document Description
The database consists of 1,000 short texts, each having 10-40 words  Out of these, 350 are sarcastic and are collected as follows: (a) 103 sentences are from two popular sarcastic quote websites, (b) 76 sarcastic short movie reviews are manually extracted from the Amazon Movie Corpus BIBREF11 by two linguists  (c) 171 tweets are downloaded using the hashtag #sarcasm from Twitter  The 650 non-sarcastic texts are either downloaded from Twitter or extracted from the Amazon Movie Review corpus  The sentences do not contain words/phrases that are highly topic or culture specific  The tweets were normalized to make them linguistically well formed to avoid difficulty in interpreting social media lingo  Every sentence in our dataset carries positive or negative opinion about specific “aspects”  For example, the sentence “The movie is extremely well cast” has positive sentiment about the aspect “cast” 
The annotators were seven graduate students with science and engineering background, and possess good English proficiency  They were given a set of instructions beforehand and are advised to seek clarifications before they proceed  The instructions mention the nature of the task, annotation input method, and necessity of head movement minimization during the experiment 
Task Description
The task assigned to annotators was to read sentences one at a time and label them with with binary labels indicating the polarity (i e , positive/negative)  Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not , to rule out the Priming Effect (i e , if sarcasm is expected beforehand, processing incongruity becomes relatively easier BIBREF12 )  The setup ensures its “ecological validity” in two ways: (1) Readers are not given any clue that they have to treat sarcasm with special attention  This is done by setting the task to polarity annotation (instead of sarcasm detection)  (2) Sarcastic sentences are mixed with non sarcastic text, which does not give prior knowledge about whether the forthcoming text will be sarcastic or not 
The eye-tracking experiment is conducted by following the standard norms in eye-movement research BIBREF13   At a time, one sentence is displayed to the reader along with the “aspect” with respect to which the annotation has to be provided  While reading, an SR-Research Eyelink-1000 eye-tracker (monocular remote mode, sampling rate 500Hz) records several eye-movement parameters like fixations (a long stay of gaze) and saccade (quick jumping of gaze between two positions of rest) and pupil size 
The accuracy of polarity annotation varies between 72%-91% for sarcastic texts and 75%-91% for non-sarcastic text, showing the inherent difficulty of sentiment annotation, when sarcasm is present in the text under consideration  Annotation errors may be attributed to: (a) lack of patience/attention while reading, (b) issues related to text comprehension, and (c) confusion/indecisiveness caused due to lack of context 
For our analysis, we do not discard the incorrect annotations present in the database  Since our system eventually aims to involve online readers for sarcasm detection, it will be hard to segregate readers who misinterpret the text  We make a rational assumption that, for a particular text, most of the readers, from a fairly large population, will be able to identify sarcasm  Under this assumption, the eye-movement parameters, averaged across all readers in our setting, may not be significantly distorted by a few readers who would have failed to identify sarcasm  This assumption is applicable for both regular and multi-instance based classifiers explained in section SECREF6  
Analysis of Eye-movement Data
We observe distinct behavior during sarcasm reading, by analyzing the “fixation duration on the text” (also referred to as “dwell time” in the literature) and “scanpaths” of the readers 
Variation in the Average Fixation Duration per Word
Since sarcasm in text can be expected to induce cognitive load, it is reasonable to believe that it would require more processing time BIBREF14   Hence, fixation duration normalized over total word count should usually be higher for a sarcastic text than for a non-sarcastic one  We observe this for all participants in our dataset, with the average fixation duration per word for sarcastic texts being at least 1 5 times more than that of non-sarcastic texts  To test the statistical significance, we conduct a two-tailed t-test (assuming unequal variance) to compare the average fixation duration per word for sarcastic and non-sarcastic texts  The hypothesized mean difference is set to 0 and the error tolerance limit ( INLINEFORM0 ) is set to 0 05  The t-test analysis, presented in Table TABREF11 , shows that for all participants, a statistically significant difference exists between the average fixation duration per word for sarcasm (higher average fixation duration) and non-sarcasm (lower average fixation duration)  This affirms that the presence of sarcasm affects the duration of fixation on words 
It is important to note that longer fixations may also be caused by other linguistic subtleties (such as difficult words, ambiguity and syntactically complex structures) causing delay in comprehension, or occulomotor control problems forcing readers to spend time adjusting eye-muscles  So, an elevated average fixation duration per word may not sufficiently indicate the presence of sarcasm  But we would also like to share that, for our dataset, when we considered readability (Flesch readability ease-score BIBREF15 ), number of words in a sentence and average character per word along with the sarcasm label as the predictors of average fixation duration following a linear mixed effect model BIBREF16 , sarcasm label turned out to be the most significant predictor with a maximum slope  This indicates that average fixation duration per word has a strong connection with the text being sarcastic, at least in our dataset 
We now analyze scanpaths to gain more insights into the sarcasm comprehension process 
Analysis of Scanpaths
Scanpaths are line-graphs that contain fixations as nodes and saccades as edges; the radii of the nodes represent the fixation duration  A scanpath corresponds to a participant's eye-movement pattern while reading a particular sentence  Figure FIGREF14 presents scanpaths of three participants for the sarcastic sentence S1 and the non-sarcastic sentence S2  The x-axis of the graph represents the sequence of words a reader reads, and the y-axis represents a temporal sequence in milliseconds 
Consider a sarcastic text containing incongruous phrases A and B  Our qualitative scanpath-analysis reveals that scanpaths with respect to sarcasm processing have two typical characteristics  Often, a long regression - a saccade that goes to a previously visited segment - is observed when a reader starts reading B after skimming through A  In a few cases, the fixation duration on A and B are significantly higher than the average fixation duration per word  In sentence S1, we see long and multiple regressions from the two incongruous phrases “misconception” and “cherish”, and a few instances where phrases “always cherish” and “original misconception” are fixated longer than usual  Such eye-movement behaviors are not seen for S2 
Though sarcasm induces distinctive scanpaths like the ones depicted in Figure FIGREF14 in the observed examples, presence of such patterns is not sufficient to guarantee sarcasm; such patterns may also possibly arise from literal texts  We believe that a combination of linguistic features, readability of text and features derived from scanpaths would help discriminative machine learning models learn sarcasm better 
Features for Sarcasm Detection
We describe the features used for sarcasm detection in Table   The features enlisted under lexical,implicit incongruity and explicit incongruity are borrowed from various literature (predominantly from joshi2015harnessing)  These features are essential to separate sarcasm from other forms semantic incongruity in text (for example ambiguity arising from semantic ambiguity or from metaphors)  Two additional textual features viz  readability and word count of the text are also taken under consideration  These features are used to reduce the effect of text hardness and text length on the eye-movement patterns 
Simple Gaze Based Features
Readers' eye-movement behavior, characterized by fixations, forward saccades, skips and regressions, can be directly quantified by simple statistical aggregation (i e , either computing features for individual participants and then averaging or performing a multi-instance based learning as explained in section SECREF6 )  Since these eye-movement attributes relate to the cognitive process in reading BIBREF17 , we consider these as features in our model  Some of these features have been reported by sarcasmunderstandability for modeling sarcasm understandability of readers  However, as far as we know, these features are being introduced in NLP tasks like textual sarcasm detection for the first time  The values of these features are believed to increase with the increase in the degree of surprisal caused by incongruity in text (except skip count, which will decrease) 
Complex Gaze Based Features
For these features, we rely on a graph structure, namely “saliency graphs", derived from eye-gaze information and word sequences in the text 
For each reader and each sentence, we construct a “saliency graph”, representing the reader's attention characteristics  A saliency graph for a sentence INLINEFORM0 for a reader INLINEFORM1 , represented as INLINEFORM2 , is a graph with vertices ( INLINEFORM3 ) and edges ( INLINEFORM4 ) where each vertex INLINEFORM5 corresponds to a word in INLINEFORM6 (may not be unique) and there exists an edge INLINEFORM7 between vertices INLINEFORM8 and INLINEFORM9 if R performs at least one saccade between the words corresponding to INLINEFORM10 and INLINEFORM11  
Figure FIGREF15 shows an example of a saliency graph A saliency graph may be weighted, but not necessarily connected, for a given text (as there may be words in the given text with no fixation on them)  The “complex” gaze features derived from saliency graphs are also motivated by the theory of incongruity  For instance, Edge Density of a saliency graph increases with the number of distinct saccades, which could arise from the complexity caused by presence of sarcasm  Similarly, the highest weighted degree of a graph is expected to be higher, if the node corresponds to a phrase, incongruous to some other phrase in the text 
The Sarcasm Classifier
We interpret sarcasm detection as a binary classification problem  The training data constitutes 994 examples created using our eye-movement database for sarcasm detection  To check the effectiveness of our feature set, we observe the performance of multiple classification techniques on our dataset through a stratified 10-fold cross validation  We also compare the classification accuracy of our system and the best available systems proposed by riloff2013sarcasm and joshi2015harnessing on our dataset  Using Weka BIBREF18 and LibSVM BIBREF19 APIs, we implement the following classifiers:
Results
Table TABREF17 shows the classification results considering various feature combinations for different classifiers and other systems  These are:
Unigram (with principal components of unigram feature vectors),
Sarcasm (the feature-set reported by joshi2015harnessing subsuming unigram features and features from other reported systems)
Gaze (the simple and complex cognitive features we introduce, along with readability and word count features), and
Gaze+Sarcasm (the complete set of features) 
For all regular classifiers, the gaze features are averaged across participants and augmented with linguistic and sarcasm related features  For the MILR classifier, the gaze features derived from each participant are augmented with linguistic features and thus, a multi instance “bag” of features is formed for each sentence in the training data  This multi-instance dataset is given to an MILR classifier, which follows the standard multi instance assumption to derive class-labels for each bag 
For all the classifiers, our feature combination outperforms the baselines (considering only unigram features) as well as BIBREF3 , with the MILR classifier getting an F-score improvement of 3 7% and Kappa difference of 0 08  We also achieve an improvement of 2% over the baseline, using SVM classifier, when we employ our feature set  We also observe that the gaze features alone, also capture the differences between sarcasm and non-sarcasm classes with a high-precision but a low recall 
To see if the improvement obtained is statistically significant over the state-of-the art system with textual sarcasm features alone, we perform McNemar test  The output of the SVM classifier using only linguistic features used for sarcasm detection by joshi2015harnessing and the output of the MILR classifier with the complete set of features are compared, setting threshold INLINEFORM0   There was a significant difference in the classifier's accuracy with p(two-tailed) = 0 02 with an odds-ratio of 1 43, showing that the classification accuracy improvement is unlikely to be observed by chance in 95% confidence interval 
Considering Reading Time as a Cognitive Feature along with Sarcasm Features
One may argue that, considering simple measures of reading effort like “reading time” as cognitive feature instead of the expensive eye-tracking features for sarcasm detection may be a cost-effective solution  To examine this, we repeated our experiments with “reading time” considered as the only cognitive feature, augmented with the textual features  The F-scores of all the classifiers turn out to be close to that of the classifiers considering sarcasm feature alone and the difference in the improvement is not statistically significant ( INLINEFORM0 )  One the other hand, F-scores with gaze features are superior to the F-scores when reading time is considered as a cognitive feature 
How Effective are the Cognitive Features
We examine the effectiveness of cognitive features on the classification accuracy by varying the input training data size  To examine this, we create a stratified (keeping the class ratio constant) random train-test split of 80%:20%  We train our classifier with 100%, 90%, 80% and 70% of the training data with our whole feature set, and the feature combination from joshi2015harnessing  The goodness of our system is demonstrated by improvements in F-score and Kappa statistics, shown in Figure FIGREF22  
We further analyze the importance of features by ranking the features based on (a) Chi squared test, and (b) Information Gain test, using Weka's attribute selection module  Figure FIGREF23 shows the top 20 ranked features produced by both the tests  For both the cases, we observe 16 out of top 20 features to be gaze features  Further, in each of the cases, Average Fixation Duration per Word and Largest Regression Position are seen to be the two most significant features 
Example Cases
Table TABREF21 shows a few example cases from the experiment with stratified 80%-20% train-test split 
Example sentence 1 is sarcastic, and requires extra-linguistic knowledge (about poor living conditions at Manchester)  Hence, the sarcasm detector relying only on textual features is unable to detect the underlying incongruity  However, our system predicts the label successfully, possibly helped by the gaze features 
Similarly, for sentence 2, the false sense of presence of incongruity (due to phrases like “Helped me” and “Can't stop”) affects the system with only linguistic features  Our system, though, performs well in this case also 
Sentence 3 presents a false-negative case where it was hard for even humans to get the sarcasm  This is why our gaze features (and subsequently the complete set of features) account for erroneous prediction 
In sentence 4, gaze features alone false-indicate presence of incongruity, whereas the system predicts correctly when gaze and linguistic features are taken together 
From these examples, it can be inferred that, only gaze features would not have sufficed to rule out the possibility of detecting other forms of incongruity that do not result in sarcasm 
Error Analysis
Errors committed by our system arise from multiple factors, starting from limitations of the eye-tracker hardware to errors committed by linguistic tools and resources  Also, aggregating various eye-tracking parameters to extract the cognitive features may have caused information loss in the regular classification setting 
Conclusion
In the current work, we created a novel framework to detect sarcasm, that derives insights from human cognition, that manifests over eye movement patterns  We hypothesized that distinctive eye-movement patterns, associated with reading sarcastic text, enables improved detection of sarcasm  We augmented traditional linguistic features with cognitive features obtained from readers' eye-movement data in the form of simple gaze-based features and complex features derived from a graph structure  This extended feature-set improved the success rate of the sarcasm detector by 3 7%, over the best available system  Using cognitive features in an NLP Processing system like ours is the first proposal of its kind 
Our general approach may be useful in other NLP sub-areas like sentiment and emotion analysis, text summarization and question answering, where considering textual clues alone does not prove to be sufficient  We propose to augment this work in future by exploring deeper graph and gaze features  We also propose to develop models for the purpose of learning complex gaze feature representation, that accounts for the power of individual eye movement patterns along with the aggregated patterns of eye movements 
Acknowledgments
We thank the members of CFILT Lab, especially Jaya Jha and Meghna Singh, and the students of IIT Bombay for their help and support 
Context 2: Introduction
Data imbalance is a common issue in a variety of NLP tasks such as tagging and machine reading comprehension  Table TABREF3 gives concrete examples: for the Named Entity Recognition (NER) task BIBREF2, BIBREF3, most tokens are backgrounds with tagging class $O$  Specifically, the number of tokens tagging class $O$ is 5 times as many as those with entity labels for the CoNLL03 dataset and 8 times for the OntoNotes5 0 dataset; Data-imbalanced issue is more severe for MRC tasks BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8 with the value of negative-positive ratio being 50-200 
Data imbalance results in the following two issues: (1) the training-test discrepancy: Without balancing the labels, the learning process tends to converge to a point that strongly biases towards class with the majority label  This actually creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function while at test time, F1 score concerns more about positive examples; (2) the overwhelming effect of easy-negative examples  As pointed out by meng2019dsreg, significantly large number of negative examples also means that the number of easy-negative example is large  The huge number of easy examples tends to overwhelm the training, making the model not sufficiently learned to distinguish between positive examples and hard-negative examples  The cross-entropy objective (CE for short) or maximum likelihood (MLE) objective, which is widely adopted as the training objective for data-imbalanced NLP tasks BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, handles neither of the issues 
To handle the first issue, we propose to replace CE or MLE with losses based on the Sørensen–Dice coefficient BIBREF0 or Tversky index BIBREF1  The Sørensen–Dice coefficient, dice loss for short, is the harmonic mean of precision and recall  It attaches equal importance to false positives (FPs) and false negatives (FNs) and is thus more immune to data-imbalanced datasets  Tversky index extends dice loss by using a weight that trades precision and recall, which can be thought as the approximation of the $F_{\beta }$ score, and thus comes with more flexibility  Therefore, We use dice loss or Tversky index to replace CE loss to address the first issue 
Only using dice loss or Tversky index is not enough since they are unable to address the dominating influence of easy-negative examples  This is intrinsically because dice loss is actually a hard version of the F1 score  Taking the binary classification task as an example, at test time, an example will be classified as negative as long as its probability is smaller than 0 5, but training will push the value to 0 as much as possible  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones  Inspired by the idea of focal loss BIBREF16 in computer vision, we propose a dynamic weight adjusting strategy, which associates each training example with a weight in proportion to $(1-p)$, and this weight dynamically changes as training proceeds  This strategy helps to deemphasize confident examples during training as their $p$ approaches the value of 1, makes the model attentive to hard-negative examples, and thus alleviates the dominating effect of easy-negative examples 
Combing both strategies, we observe significant performance boosts on a wide range of data imbalanced NLP tasks  Notably, we are able to achieve SOTA results on CTB5 (97 92, +1 86), CTB6 (96 57, +1 80) and UD1 4 (96 98, +2 19) for the POS task; SOTA results on CoNLL03 (93 33, +0 29), OntoNotes5 0 (92 07, +0 96)), MSRA 96 72(+0 97) and OntoNotes4 0 (84 47,+2 36) for the NER task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification 
The rest of this paper is organized as follows: related work is presented in Section 2  We describe different training objectives in Section 3  Experimental results are presented in Section 4  We perform ablation studies in Section 5, followed by a brief conclusion in Section 6 
Related Work ::: Data Resample
The idea of weighting training examples has a long history  Importance sampling BIBREF17 assigns weights to different samples and changes the data distribution  Boosting algorithms such as AdaBoost BIBREF18 select harder examples to train subsequent classifiers  Similarly, hard example mining BIBREF19 downsamples the majority class and exploits the most difficult examples  Oversampling BIBREF20, BIBREF21 is used to balance the data distribution  Another line of data resampling is to dynamically control the weights of examples as training proceeds  For example, focal loss BIBREF16 used a soft weighting scheme that emphasizes harder examples during training  In self-paced learning BIBREF22, example weights are obtained through optimizing the weighted training loss which encourages learning easier examples first  At each training step, self-paced learning algorithm optimizes model parameters and example weights jointly  Other works BIBREF23, BIBREF24 adjusted the weights of different training examples based on training loss  Besides, recent work BIBREF25, BIBREF26 proposed to learn a separate network to predict sample weights 
Related Work ::: Data Imbalance Issue in Object Detection
The background-object label imbalance issue is severe and thus well studied in the field of object detection BIBREF27, BIBREF28, BIBREF29, BIBREF30, BIBREF31  The idea of hard negative mining (HNM) BIBREF30 has gained much attention recently  shrivastava2016ohem proposed the online hard example mining (OHEM) algorithm in an iterative manner that makes training progressively more difficult, and pushes the model to learn better  ssd2016liu sorted all of the negative samples based on the confidence loss and picking the training examples with the negative-positive ratio at 3:1  pang2019rcnn proposed a novel method called IoU-balanced sampling and aploss2019chen designed a ranking model to replace the conventional classification task with a average-precision loss to alleviate the class imbalance issue  The efforts made on object detection have greatly inspired us to solve the data imbalance issue in NLP 
Losses ::: Notation
For illustration purposes, we use the binary classification task to demonstrate how different losses work  The mechanism can be easily extended to multi-class classification 
Let $\lbrace x_i\rbrace $ denote a set of instances  Each $x_i$ is associated with a golden label vector $y_i = [y_{i0},y_{i1} ]$, where $y_{i1}\in \lbrace 0,1\rbrace $ and $y_{i0}\in \lbrace 0,1\rbrace $ respectively denote the positive and negative classes, and thus $y_i$ can be either $[0,1]$ or $[0,1]$  Let $p_i = [p_{i0},p_{i1} ]$ denote the probability vector, and $p_{i1}$ and $p_{i0}$ respectively denote the probability that a model assigns the positive and negative label to $x_i$ 
Losses ::: Cross Entropy Loss
The vanilla cross entropy (CE) loss is given by:
As can be seen from Eq DISPLAY_FORM8, each $x_i$ contributes equally to the final objective  Two strategies are normally used to address the the case where we wish that not all $x_i$ are treated equal: associating different classes with different weighting factor $\alpha $ or resampling the datasets  For the former, Eq DISPLAY_FORM8 is adjusted as follows:
where $\alpha _i\in [0,1]$ may be set by the inverse class frequency or treated as a hyperparameter to set by cross validation  In this work, we use $\lg (\frac{n-n_t}{n_t}+K)$ to calculate the coefficient $\alpha $, where $n_t$ is the number of samples with class $t$ and $n$ is the total number of samples in the training set  $K$ is a hyperparameter to tune  The data resampling strategy constructs a new dataset by sampling training examples from the original dataset based on human-designed criteria, e g , extract equal training samples from each class  Both strategies are equivalent to changing the data distribution and thus are of the same nature  Empirically, these two methods are not widely used due to the trickiness of selecting $\alpha $ especially for multi-class classification tasks and that inappropriate selection can easily bias towards rare classes BIBREF32 
Losses ::: Dice coefficient and Tversky index
Sørensen–Dice coefficient BIBREF0, BIBREF33, dice coefficient (DSC) for short, is a F1-oriented statistic used to gauge the similarity of two sets  Given two sets $A$ and $B$, the dice coefficient between them is given as follows:
In our case, $A$ is the set that contains of all positive examples predicted by a specific model, and $B$ is the set of all golden positive examples in the dataset  When applied to boolean data with the definition of true positive (TP), false positive (FP), and false negative (FN), it can be then written as follows:
For an individual example $x_i$, its corresponding DSC loss is given as follows:
As can be seen, for a negative example with $y_{i1}=0$, it does not contribute to the objective  For smoothing purposes, it is common to add a $\gamma $ factor to both the nominator and the denominator, making the form to be as follows:
As can be seen, negative examples, with $y_{i1}$ being 0 and DSC being $\frac{\gamma }{ p_{i1}+\gamma }$, also contribute to the training  Additionally, milletari2016v proposed to change the denominator to the square form for faster convergence, which leads to the following dice loss (DL):
Another version of DL is to directly compute set-level dice coefficient instead of the sum of individual dice coefficient  We choose the latter due to ease of optimization 
Tversky index (TI), which can be thought as the approximation of the $F_{\beta }$ score, extends dice coefficient to a more general case  Given two sets $A$ and $B$, tversky index is computed as follows:
Tversky index offers the flexibility in controlling the tradeoff between false-negatives and false-positives  It degenerates to DSC if $\alpha =\beta =0 5$  The Tversky loss (TL) for the training set $\lbrace x_i,y_i\rbrace $ is thus as follows:
Losses ::: Self-adusting Dice Loss
Consider a simple case where the dataset consists of only one example $x_i$, which is classified as positive as long as $p_{i1}$ is larger than 0 5  The computation of $F1$ score is actually as follows:
Comparing Eq DISPLAY_FORM14 with Eq DISPLAY_FORM22, we can see that Eq DISPLAY_FORM14 is actually a soft form of $F1$, using a continuous $p$ rather than the binary $\mathbb {I}( p_{i1}>0 5)$  This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily  Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones, which has a huge negative effect on the final F1 performance 
To address this issue, we propose to multiply the soft probability $p$ with a decaying factor $(1-p)$, changing Eq DISPLAY_FORM22 to the following form:
One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds  The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples  For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them  Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified 
A close look at Eq DISPLAY_FORM14 reveals that it actually mimics the idea of focal loss (FL for short) BIBREF16 for object detection in vision  Focal loss was proposed for one-stage object detector to handle foreground-background tradeoff encountered during training  It down-weights the loss assigned to well-classified examples by adding a $(1-p)^{\beta }$ factor, leading the final loss to be $(1-p)^{\beta }\log p$ 
In Table TABREF18, we show the losses used in our experiments, which is described in the next section 
Experiments
We evaluate the proposed method on four NLP tasks: part-of-speech tagging, named entity recognition, machine reading comprehension and paraphrase identification  Baselines in our experiments are optimized by using the standard cross-entropy training objective 
Experiments ::: Part-of-Speech Tagging
Part-of-speech tagging (POS) is the task of assigning a label (e g , noun, verb, adjective) to each word in a given text  In this paper, we choose BERT as the backbone and conduct experiments on three Chinese POS datasets  We report the span-level micro-averaged precision, recall and F1 for evaluation  Hyperparameters are tuned on the corresponding development set of each dataset 
Experiments ::: Part-of-Speech Tagging ::: Datasets
We conduct experiments on the widely used Chinese Treebank 5 0, 6 0 as well as UD1 4 
CTB5 is a Chinese dataset for tagging and parsing, which contains 507,222 words, 824,983 characters and 18,782 sentences extracted from newswire sources 
CTB6 is an extension of CTB5, containing 781,351 words, 1,285,149 characters and 28,295 sentences 
UD is the abbreviation of Universal Dependencies, which is a framework for consistent annotation of grammar (parts of speech, morphological features, and syntactic dependencies) across different human languages  In this work, we use UD1 4 for Chinese POS tagging 
Experiments ::: Part-of-Speech Tagging ::: Baselines
We use the following baselines:
Joint-POS: shao2017character jointly learns Chinese word segmentation and POS 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice 
Bert-Tagger: devlin2018bert treats part-of-speech as a tagging task
Context 3: Introduction
Back to 42 BC, the philosopher Cicero has raised the issue that although there were many Oratory classes, there were none for Conversational skills BIBREF0   He highlighted how important they were not only for politics, but also for educational purpose  Among other conversational norms, he claimed that people should be able to know when to talk in a conversation, what to talk depending on the subject of the conversation, and that they should not talk about themselves 
Norms such as these may become social conventions and are not learnt at home or at school  Social conventions are dynamic and may change according to context, culture and language  In online communication, new commonsense practices are evolved faster and accepted as a norm BIBREF1 , BIBREF2   There is not a discipline for that on elementary or high schools and there are few linguistics researchers doing research on this field 
On the other hand, within the Artificial Intelligence area, some Conversational Systems have been created in the past decades since the test proposed by Alan Turing in 1950  The test consists of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from that of a human BIBREF3   Turing proposed that a human evaluator would judge natural language conversations between a human and a machine that is designed to generate human-like responses  Since then, many systems have been created to pass the Turing's test  Some of them have won prizes, some not BIBREF4   Although in this paper we do not focus on creating a solution that is able to build conversational systems that pass the Turing's test, we focus on NDS  From BIBREF5 , "NDS are systems that try to improve usability and user satisfaction by imitating human behavior"  We refer to Conversational Systems as NDS, where the dialogues are expressed as natural language texts, either from artificial intelligent agents (a k a  bots) or from humans 
That said, the current popular name to systems that have the ability to make a conversation with humans using natural language is Chatbot  Chatbots are typically used in conversational systems for various practical purposes, including customer service or information acquisition  Chatbots are becoming more widely used by social media software vendors  For example, Facebook recently announced that it would make Facebook Messenger (its 900-million-user messaging app by 2016), into a full-fledged platform that allows businesses to communicate with users via chatbots  Google is also building a new mobile-messaging service that uses artificial intelligence know-how and chatbot technology  In addition, according to the Wall Street Journal, there are more than 2 billion users of mobile apps  Still, people can be reluctant to install apps  So it is believed that social messaging can be a platform and chatbots may provide a new conversational interface for interacting with online services, as chatbots are easier to build and deploy than apps BIBREF6  
China seems to be the place where chatbots adoption and use is most advanced today  For example, China's popular WeChat messaging platform can take payments, scan QR codes, and integrate chatbot systems  WeChat integrates e-mail, chat, videocalls and sharing of large multimedia files  Users can book flights or hotels using a mixed, multimedia interaction with active bots  WeChat was first released in 2011 by Tecent, a Chinese online-gaming and social-media firm, and today more than 700 million people use it, being one of the most popular messaging apps in the world (The Economist 2016)  WeChat has a mixture of real-live customer service agents and automated replies (Olson 2016) 
Still, current existing chatbot engines do not properly handle a group chat with many users and many chatbots  This makes the chatbots considerably less social, which is a problem since there is a strong demand of having social chatbots that are able to provide different kinds of services, from traveling packages to finance advisors  This happens because there is a lack of methods and tools to design and engineer the coordination and mediation among chatbots and humans, as we present in Sections 2 and 3  In this paper, we refer to conversational systems that are able to interact with one or more people or chatbots in a multi-party chat as MPCS  Altogether, this paper is not meant to advance the state of the art on the norms for MPCS  Instead, the main contributions of this paper are threefold:
We then present some discussion and future work in the last section 
Challenges on Chattering
There are plenty of challenges in conversation contexts, and even bigger ones when people and machines participate in those contexts  Conversation is a specialized form of interaction, which follows social conventions  Social interaction makes it possible to inform, context, create, ratify, refute, and ascribe, among other things, power, class, gender, ethnicity, and culture BIBREF2   Social structures are the norms that emerge from the contact people have with others BIBREF7 , for example, the communicative norms of a negotiation, taking turns in a group, the cultural identity of a person, or power relationships in a work context 
Conventions, norms and patterns from everyday real conversations are applied when designing those systems to result in adoption and match user's expectations  BIBREF8 describes implicit interactions in a framework of interactions between humans and machines  The framework is based on the theory of implicit interactions which posits that people rely on conventions of interaction to communicate queries, offers, responses, and feedback to one another  Conventions and patterns drive our expectations about interactive behaviors  This framework helps designers and developers create interactions that are more socially appropriate  According to the author, we have interfaces which are based on explicit interaction and implicit ones  The explicit are the interactions or interfaces where people rely on explicit input and output, whereas implicit interactions are the ones that occur without user awareness of the computer behavior 
Social practices and actions are essential for a conversation to take place during the turn-by-turn moments of communication  BIBREF9 highlights that a distinguishing feature of ordinary conversation is "the local, moment-by-moment management of the distribution of turns, of their size, and what gets done in them, those things being accomplished in the course of each current speaker's turn " Management of turns and subject change in each course is a situation that occurs in real life conversations based on circumstances (internal and external) to speakers in a dialogue  Nowadays, machines are not prepared to fully understand context and change the course of conversations as humans  Managing dialogues with machines is challenging, which increases even more when more than one conversational agent is part of the same conversation  Some of those challenges in the dialogue flow were addressed by BIBREF10   According to them, we have system-initiative, user-initiative, and mixed-initiative systems 
In the first case, system-initiative systems restrict user options, asking direct questions, such as (Table TABREF5 ): "What is the initial amount of investment?" Doing so, those types of systems are more successful and easier to answer to  On the other hand, user-initiative systems are the ones where users have freedom to ask what they wish  In this context, users may feel uncertain of the capabilities of the system and starting asking questions or requesting information or services which might be quite far from the system domain and understanding capacity, leading to user frustration  There is also a mixed-initiative approach, that is, a goal-oriented dialogue which users and computers participate interactively using a conversational paradigm  Challenges of this last classification are to understand interruptions, human utterances, and unclear sentences that were not always goal-oriented 
The dialog in Table TABREF5 has the system initiative in a question and answer mode, while the one in Table TABREF7 is a natural dialogue system where both the user and the system take the initiative  If we add another user in the chat, then we face other challenges 
In Table TABREF12 , line 4, the user U1 invites another person to the chat and the system does not reply to this utterance, nor to utterances on lines 6, 7 and 8 which are the ones when only the users (wife and husband) should reply to  On the other hand, when the couple agrees on the period and initial value of the investment (line 9), then the system S1 (at the time the only system in the chat) replies indicating that it will invite more systems (chatbots) that are experts on this kind of pair INLINEFORM0 period, initial value INLINEFORM1   They then join the chat and start interacting with each other  At the end, on line 17, the user U2 interacts with U1 and they agree with the certificate option  Then, the chatbot responsible for that, S3, is the only one that replies indicating how to invest 
Table TABREF12 is one example of interactions on which the chatbots require knowledge of when to reply given the context of the dialog  In general, we acknowledge that exist four dimensions of understanding and replying to an utterance in MPCS which a chatbot that interacts in a multi-party chat group should fulfill:
In the next section we present the state of the art and how they fullfil some of these dimensions 
Conversational Systems
In this section we discuss the state of the art on conversational systems in three perspectives: types of interactions, types of architecture, and types of context reasoning  Then we present a table that consolidates and compares all of them 
ELIZA BIBREF11 was one of the first softwares created to understand natural language processing  Joseph Weizenbaum created it at the MIT in 1966 and it is well known for acting like a psychotherapist and it had only to reflect back onto patient's statements  ELIZA was created to tackle five "fundamental technical problems": the identification of critical words, the discovery of a minimal context, the choice of appropriate transformations, the generation of appropriate responses to the transformation or in the absence of critical words, and the provision of an ending capacity for ELIZA scripts 
Right after ELIZA came PARRY, developed by Kenneth Colby, who is psychiatrist at Stanford University in the early 1970s  The program was written using the MLISP language (meta-lisp) on the WAITS operating system running on a DEC PDP-10 and the code is non-portable  Parts of it were written in PDP-10 assembly code and others in MLISP  There may be other parts that require other language translators  PARRY was the first system to pass the Turing test - the psychiatrists were able to make the correct identification only 48 percent of the time, which is the same as a random guessing 
A L I C E  (Artificial Linguistic Internet Computer Entity) BIBREF12 appeared in 1995 but current version utilizes AIML, an XML language designed for creating stimulus-response chat robots BIBREF13   A L I C E  bot has, at present, more than 40,000 categories of knowledge, whereas the original ELIZA had only about 200  The program is unable to pass the Turing test, as even the casual user will often expose its mechanistic aspects in short conversations 
Cleverbot (1997-2014) is a chatbot developed by the British AI scientist Rollo Carpenter  It passed the 2011 Turing Test at the Technique Techno-Management Festival held by the Indian Institute of Technology Guwahati  Volunteers participate in four-minute typed conversations with either Cleverbot or humans, with Cleverbot voted 59 3 per cent human, while the humans themselves were rated just 63 3 per cent human BIBREF14  
Types of Interactions
Although most part of the research literature focuses on the dialogue of two persons, the reality of everyday life interactions shows a substantial part of multi-user conversations, such as in meetings, classes, family dinners, chats in bars and restaurants, and in almost every collaborative or competitive environment such as hospitals, schools, offices, sports teams, etc  The ability of human beings to organize, manage, and (mostly) make productive such complex interactive structures which are multi-user conversations is nothing less than remarkable  The advent of social media platforms and messaging systems such as WhatsApp in the first 15 years of the 21st century expanded our ability as a society to have asynchronous conversations in text form, from family and friends chatgroups to whole nations conversing in a highly distributed form in social media BIBREF15  
In this context, many technological advances in the early 2010s in natural language processing (spearheaded by the IBM Watson's victory in Jeopardy BIBREF16 ) spurred the availability in the early 2010s of text-based chatbots in websites and apps (notably in China BIBREF17 ) and spoken speech interfaces such as Siri by Apple, Cortana by Microsoft, Alexa by Amazon, and Allo by Google  However, the absolute majority of those chatbot deployments were in contexts of dyadic dialog, that is, a conversation between a single chatbot with a single user  Most of the first toolkits for chatbot design and development of this initial period implicit assume that an utterance from the user is followed by an utterance of the chatbot, which greatly simplifies the management of the conversation as discussed in more details later  Therefore, from the interaction point of view, there are two types: 1) one in which the chatbot was designed to chat with one person or chatbot, and 2) other in which the chatbot can interact with more than two members in the chat 
Dyadic Chatbot
A Dyadic Chatbot is a chatbot that does know when to talk  If it receives an utterance, it will always handle and try to reply to the received utterance  For this chatbot to behave properly, either there are only two members in the chat, and the chatbot is one of them, or there are more, but the chatbot replies only when its name or nickname is mentioned  This means that a dyadic chatbot does not know how to coordinate with many members in a chat group  It lacks the social ability of knowing when it is more suitable to answer or not  Also, note that we are not considering here the ones that would use this social ability as an advantage in the conversation, because if the chatbot is doing with this intention, it means that the chatbot was designed to be aware of the social issues regarding a chat with multiple members, which is not the case of a dyadic chatbot  Most existing chatbots, from the first system, ELIZA BIBREF11 , until modern state-of-the-art ones fall into this category 
Multiparty Conversations
In multiparty conversations between people and computer systems, natural language becomes the communication protocol exchanged not only by the human users, but also among the bots themselves  When every actor, computer or user, understands human language and is able to engage effectively in a conversation, a new, universal computer protocol of communication is feasible, and one for which people are extremely good at 
There are many differences between dyadic and multiparty conversations, but chiefly among them is turn-taking, that is, how a participant determines when it is appropriate to make an utterance and how that is accomplished  There are many social settings, such as assemblies, debates, one-channel radio communications, and some formal meetings, where there are clear and explicit norms of who, when, and for long a participant can speak 
The state of the art for the creation of chatbots that can participate on multiparty conversations currently is a combination of the research on the creation of chatbots and research on the coordination or governance of multi-agents systems  A definition that mixes both concepts herein present is: A chatbot is an agent that interacts through natural language  Although these areas complement each other, there is a lack of solutions for creating multiparty-aware chatbots or governed chatbots, which can lead to higher degree of system trust 
Multi-Dyadic Chatbots
Turn-taking in generic, multiparty spoken conversation has been studied by, for example, Sacks et al  BIBREF18   In broad terms, it was found that participants in general do not overlap their utterances and that the structure of the language and the norms of conversation create specific moments, called transition-relevance places, where turns can occur  In many cases, the last utterances make clear to the participants who should be the next speaker (selected-next-speaker), and he or she can take that moment to start to talk  Otherwise, any other participant can start speaking, with preference for the first starter to get the turn; or the current speaker can continue BIBREF18  
A key part of the challenge is to determine whether the context of the conversation so far have or have not determined the next speaker  In its simplest form, a vocative such as the name of the next speaker is uttered  Also, there is a strong bias towards the speaker before the current being the most likely candidate to be the next speaker 
In general the detection of transition-relevance places and of the selected-next-speaker is still a challenge for speech-based machine conversational systems  However, in the case of text message chats, transition-relevance places are often determined by the acting of posting a message, so the main problem facing multiparty-enabled textual chatbots is in fact determining whether there is and who is the selected-next-speaker  In other words, chatbots have to know when to shut up  Bohus and Horowitz BIBREF19 have proposed a computational probabilistic model for speech-based systems, but we are not aware of any work dealing with modeling turn-taking in textual chats 
Coordination of Multi-Agent Systems
A multi-agent system (MAS) can be defined as a computational environment in which individual software agents interact with each other, in a cooperative manner, or in a competitive manner, and sometimes autonomously pursuing their individual goals  During this process, they access the environment's resources and services and occasionally produce results for the entities that initiated these software agents  As the agents interact in a concurrent, asynchronous and decentralized manner, this kind of system can be categorized as a complex system BIBREF20  
Research in the coordination of multi-agent systems area does not address coordination using natural dialogue, as usually all messages are structured and formalized so the agents can reason and coordinate themselves  On the other hand, chatbots coordination have some relations with general coordination mechanisms of multi-agent systems in that they specify and control interactions between agents  However, chatbots coordination mechanisms is meant to regulate interactions and actions from a social perspective, whereas general coordination languages and mechanisms focus on means for expressing synchronization and coordination of activities and exchange of information, at a lower computational level 
In open multi-agent systems the development takes place without a centralized control, thus it is necessary to ensure the reliability of these systems in a way that all the interactions between agents will occur according to the specification and that these agents will obey the specified scenario  For this, these applications must be built upon a law-governed architecture 
Minsky published the first ideas about laws in 1987 BIBREF21   Considering that a law is a set of norms that govern the interaction, afterwards, he published a seminal paper with the Law-Governed Interaction (LGI) conceptual model about the role of interaction laws on distributed systems BIBREF22   Since then, he conducted further work and experimentation based on those ideas BIBREF23   Although at the low level a multiparty conversation system is a distributed system and the LGI conceptual model can be used in a variety of application domains, it is composed of abstractions basically related to low level information about communication issues of distributed systems (like the primitives disconnected, reconnected, forward, and sending or receiving of messages), lacking the ability to express high level information of social systems 
Following the same approach, the Electronic Institution (EI) BIBREF24 solution also provides support for interaction norms  An EI has a set of high-level abstractions that allow for the specification of laws using concepts such as agent roles, norms and scenes 
Still at the agent level but more at the social level, the XMLaw description language and the M-Law framework BIBREF25 BIBREF26 were proposed and developed to support law-governed mechanism  They implement a law enforcement approach as an object-oriented framework and it allows normative behavior through the combination between norms and clocks  The M-Law framework BIBREF26 works by intercepting messages exchanged between agents, verifying the compliance of the messages with the laws and subsequently redirecting the message to the real addressee, if the laws allow it  If the message is not compliant, then the mediator blocks the message and applies the consequences specified in the law, if any  They are called laws in the sense that they enforce the norms, which represent what can be done (permissions), what cannot be done (prohibitions) and what must be done (obligations) 
Coordinated Aware Chatbots in a Multiparty Conversation
With regard to chatbot engines, there is a lack of research directed to building coordination laws integrated with natural language  To the best of our knowledge, the architecture proposed in this paper is the first one in the state of the art designed to support the design and development of coordinated aware chatbots in a multiparty conversation 
Types of Architectures
There are mainly three types of architectures when building conversational systems: totally rule-oriented, totally data-oriented, and a mix of rules and data-oriented 
Rule-oriented
A rule-oriented architecture provides a manually coded reply for each recognized utterance  Classical examples of rule-based chatbots include Eliza and Parry  Eliza could also extract some words from sentences and then create another sentence with these words based on their syntatic functions  It was a rule-based solution with no reasoning  Eliza could not "understand" what she was parsing  More sophisticated rule-oriented architectures contain grammars and mappings for converting sentences to appropriate sentences using some sort of knowledge  They can be implemented with propositional logic or first-order logic (FOL)  Propositional logic assumes the world contains facts (which refer to events, phenomena, symptoms or activities)  Usually, a set of facts (statements) is not sufficient to describe a domain in a complete manner  On the other hand, FOL assumes the world contains Objects (e g , people, houses, numbers, etc ), Relations (e g  red, prime, brother of, part of, comes between, etc ), and Functions (e g  father of, best friend, etc ), not only facts as in propositional logic  Moreover, FOL contains predicates, quantifiers and variables, which range over individuals (which are domain of discourse) 
Prolog (from French: Programmation en Logique) was one of the first logic programming languages (created in the 1970s), and it is one of the most important languages for expressing phrases, rules and facts  A Prolog program consists of logical formulas and running a program means proving a theorem  Knowledge bases, which include rules in addition to facts, are the basis for most rule-oriented chatbots created so far 
In general, a rule is presented as follows: DISPLAYFORM0
Prolog made it possible to perform the language of Horn clauses (implications with only one conclusion)  The concept of Prolog is based on predicate logic, and proving theorems involves a resolute system of denials  Prolog can be distinguished from classic programming languages due to its possibility of interpreting the code in both a procedural and declarative way  Although Prolog is a set of specifications in FOL, it adopts the closed-world assumption, i e  all knowledge of the world is present in the database  If a term is not in the database, Prolog assumes it is false 
In case of Prolog, the FOL-based set of specifications (formulas) together with the facts compose the knowledge base to be used by a rule-oriented chatbot  However an Ontology could be used  For instance, OntBot BIBREF27 uses mapping technique to transform ontologies and knowledge into relational database and then use that knowledge to drive its chats  One of the main issues currently facing such a huge amount of ontologies stored in a database is the lack of easy to use interfaces for data retrieval, due to the need to use special query languages or applications 
In rule-oriented chatbots, the degree of intelligent behavior depends on the knowledge base size and quality (which represents the information that the chatbot knows), poor ones lead to weak chatbot responses while good ones do the opposite  However, good knowledge bases may require years to be created, depending on the domain 
Data-oriented
As opposed to rule-oriented architectures, where rules have to be explicitly defined, data-oriented architectures are based on learning models from samples of dialogues, in order to reproduce the behavior of the interaction that are observed in the data  Such kind of learning can be done by means of machine learning approach, or by simply extracting rules from data instead of manually coding them 
Among the different technologies on which these system can be based, we can highlight classical information retrieval algorithms, neural networks BIBREF28 , Hidden Markov Models (HMM) BIBREF29 , and Partially Observable Markov Decision Process (POMDP) BIBREF30   Examples include Cleverbot and Tay BIBREF31   Tay was a chatbot developed by Microsoft that after one day live learning from interaction with teenagers on Twitter, started replying impolite utterances  Microsoft has developed others similar chatbots in China (Xiaoice) and in Japan (Rinna)  Microsoft has not associated its publications with these chatbots, but they have published a data-oriented approach BIBREF32 that proposes a unified multi-turn multi-task spoken language understanding (SLU) solution capable of handling multiple context sensitive classification (intent determination) and sequence labeling (slot filling) tasks simultaneously  The proposed architecture is based on recurrent convolutional neural networks (RCNN) with shared feature layers and globally normalized sequence modeling components 
A survey of public available corpora for can be found in BIBREF33   A corpus can be classified into different categories, according to: the type of data, whether it is spoken dialogues, transcripts of spoken dialogues, or directly written; the type of interaction, if it is human-human or human-machine; and the domain, whether it is restricted or unconstrained  Two well-known corpora are the Switchboard dataset, which consists of transcripts of spoken, unconstrained, dialogues, and the set of tasks for the Dialog State Tracking Challenge (DSTC), which contain more constrained tasks, for instance the restaurant and travel information sets 
Rule and Data-oriented
The model of learning in current A L I C E  BIBREF13 is incremental or/and interactive learning because a person monitors the robot's conversations and creates new AIML content to make the responses more appropriate, accurate, believable, "human", or whatever he/she intends  There are algorithms for automatic detection of patterns in the dialogue data and this process provides the person with new input patterns that do not have specific replies yet, permitting a process of almost continuous supervised refinement of the bot 
As already mentioned, A L I C E  consists of roughly 41,000 elements called categories which is the basic unit of knowledge in AIML  Each category consists of an input question, an output answer, and an optional context  The question, or stimulus, is called the pattern  The answer, or response, is called the template  The two types of optional context are called that and topic  The keyword that refers to the robot's previous utterance  The AIML pattern language consists only of words, spaces, and the wildcard symbols "_" and "*"  The words may consist only of letters and numerals  The pattern language is case invariant  Words are separated by a single space, and the wildcard characters function like words, similar to the initial pattern matching strategy of the Eliza system  More generally, AIML tags transform the reply into a mini computer program which can save data, activate other programs, give conditional responses, and recursively call the pattern matcher to insert the responses from other categories  Most AIML tags in fact belong to this template side sublanguage BIBREF13  
AIML language allows:
Symbolic reduction: Reduce complex grammatical forms to simpler ones 
Divide and conquer: Split an input into two or more subparts, and combine the responses to each 
Synonyms: Map different ways of saying the same thing to the same reply 
Spelling or grammar corrections: the bot both corrects the client input and acts as a language tutor 
Detecting keywords anywhere in the input that act like triggers for a reply 
Conditionals: Certain forms of branching to produce a reply 
Any combination of (1)-(6)

ground_truth: Unanswerable

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What are method improvements of F1 for paraphrase identification?
Context 2: 
Experiments ::: Part-of-Speech Tagging ::: Results
Table presents the experimental results on the POS task  As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i e , outperforming BERT-tagger by +1 86 in terms of F1 score on CTB5, +1 80 on CTB6 and +2 19 on UD1 4  As far as we are concerned, we are achieving SOTA performances on the three datasets  Weighted cross entropy and focal loss only gain a little performance improvement on CTB5 and CTB6, and the dice loss obtains huge gain on CTB5 but not on CTB6, which indicates the three losses are not consistently robust in resolving the data imbalance issue  The proposed DSC loss performs robustly on all the three datasets 
Experiments ::: Named Entity Recognition
Named entity recognition (NER) refers to the task of detecting the span and semantic category of entities from a chunk of text  Our implementation uses the current state-of-the-art BERT-MRC model proposed by xiaoya2019ner as a backbone  For English datasets, we use BERT$_\text{Large}$ English checkpoints, while for Chinese we use the official Chinese checkpoints  We report span-level micro-averaged precision, recall and F1-score  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Named Entity Recognition ::: Datasets
For the NER task, we consider both Chinese datasets, i e , OntoNotes4 0 BIBREF34 and MSRA BIBREF35, and English datasets, i e , CoNLL2003 BIBREF36 and OntoNotes5 0 BIBREF37 
CoNLL2003 is an English dataset with 4 entity types: Location, Organization, Person and Miscellaneous  We followed data processing protocols in BIBREF14 
English OntoNotes5 0 consists of texts from a wide variety of sources and contains 18 entity types  We use the standard train/dev/test split of CoNLL2012 shared task 
Chinese MSRA performs as a Chinese benchmark dataset containing 3 entity types  Data in MSRA is collected from news domain  Since the development set is not provided in the original MSRA dataset, we randomly split the training set into training and development splits by 9:1  We use the official test set for evaluation 
Chinese OntoNotes4 0 is a Chinese dataset and consists of texts from news domain, which has 18 entity types  In this paper, we take the same data split as wu2019glyce did 
Experiments ::: Named Entity Recognition ::: Baselines
We use the following baselines:
ELMo: a tagging model from peters2018deep 
Lattice-LSTM: lattice2018zhang constructs a word-character lattice, only used in Chinese datasets 
CVT: from kevin2018cross, which uses Cross-View Training(CVT) to improve the representations of a Bi-LSTM encoder 
Bert-Tagger: devlin2018bert treats NER as a tagging task 
Glyce-BERT: wu2019glyce combines glyph information with BERT pretraining 
BERT-MRC: The current SOTA model for both Chinese and English NER datasets proposed by xiaoya2019ner, which formulate NER as machine reading comprehension task 
Experiments ::: Named Entity Recognition ::: Results
Table shows experimental results on NER datasets  For English datasets including CoNLL2003 and OntoNotes5 0, our proposed method outperforms BERT-MRCBIBREF38 by +0 29 and +0 96 respectively  We observe huge performance boosts on Chinese datasets, achieving F1 improvements by +0 97 and +2 36 on MSRA and OntoNotes4 0, respectively  As far as we are concerned, we are setting new SOTA performances on all of the four NER datasets 
Experiments ::: Machine Reading Comprehension
Machine reading comprehension (MRC) BIBREF39, BIBREF40, BIBREF41, BIBREF40, BIBREF42, BIBREF15 has become a central task in natural language understanding  MRC in the SQuAD-style is to predict the answer span in the passage given a question and the passage  In this paper, we choose the SQuAD-style MRC task and report Extract Match (EM) in addition to F1 score on validation set  All hyperparameters are tuned on the development set of each dataset 
Experiments ::: Machine Reading Comprehension ::: Datasets
The following five datasets are used for MRC task: SQuAD v1 1, SQuAD v2 0 BIBREF4, BIBREF6 and Quoref BIBREF8 
SQuAD v1 1 and SQuAD v2 0 are the most widely used QA benchmarks  SQuAD1 1 is a collection of 100K crowdsourced question-answer pairs, and SQuAD2 0 extends SQuAD1 1 allowing no short answer exists in the provided passage 
Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems, containing 24K questions over 4 7K paragraphs from Wikipedia 
Experiments ::: Machine Reading Comprehension ::: Baselines
We use the following baselines:
QANet: qanet2018 builds a model based on convolutions and self-attention  Convolution to model local interactions and self-attention to model global interactions 
BERT: devlin2018bert treats NER as a tagging task 
XLNet: xlnet2019 proposes a generalized autoregressive pretraining method that enables learning bidirectional contexts 
Experiments ::: Machine Reading Comprehension ::: Results
Table shows the experimental results for MRC tasks  With either BERT or XLNet, our proposed DSC loss obtains significant performance boost on both EM and F1  For SQuADv1 1, our proposed method outperforms XLNet by +1 25 in terms of F1 score and +0 84 in terms of EM and achieves 87 65 on EM and 89 51 on F1 for SQuAD v2 0  Moreover, on QuoRef, the proposed method surpasses XLNet results by +1 46 on EM and +1 41 on F1  Another observation is that, XLNet outperforms BERT by a huge margin, and the proposed DSC loss can obtain further performance improvement by an average score above 1 0 in terms of both EM and F1, which indicates the DSC loss is complementary to the model structures 
Experiments ::: Paraphrase Identification
Paraphrases are textual expressions that have the same semantic meaning using different surface words  Paraphrase identification (PI) is the task of identifying whether two sentences have the same meaning or not  We use BERT BIBREF11 and XLNet BIBREF43 as backbones and report F1 score for comparison  Hyperparameters are tuned on the development set of each dataset 
Experiments ::: Paraphrase Identification ::: Datasets
We conduct experiments on two widely used datasets for PI task: MRPC BIBREF44 and QQP 
MRPC is a corpus of sentence pairs automatically extracted from online news sources, with human annotations of whether the sentence pairs are semantically equivalent  The MRPC dataset has imbalanced classes (68% positive, 32% for negative) 
QQP is a collection of question pairs from the community question-answering website Quora  The class distribution in QQP is also unbalanced (37% positive, 63% negative) 
Experiments ::: Paraphrase Identification ::: Results
Table shows the results for PI task  We find that replacing the training objective with DSC introduces performance boost for both BERT and XLNet  Using DSC loss improves the F1 score by +0 58 for MRPC and +0 73 for QQP 
Ablation Studies ::: The Effect of Dice Loss on Accuracy-oriented Tasks
We argue that the most commonly used cross-entropy objective is actually accuracy-oriented, whereas the proposed dice loss (DL) performs as a hard version of F1-score  To explore the effect of the dice loss on accuracy-oriented tasks such as text classification, we conduct experiments on the Stanford Sentiment Treebank sentiment classification datasets including SST-2 and SST-5  We fine-tune BERT$_\text{Large}$ with different training objectives  Experiment results for SST are shown in   For SST-5, BERT with CE achieves 55 57 in terms of accuracy, with DL and DSC losses slightly degrade the accuracy performance and achieve 54 63 and 55 19, respectively  For SST-2, BERT with CE achieves 94 9 in terms of accuracy  The same as SST-5, we observe a slight performance drop with DL and DSC, which means that the dice loss actually works well for F1 but not for accuracy 
Ablation Studies ::: The Effect of Hyperparameters in Tversky index
As mentioned in Section SECREF10, Tversky index (TI) offers the flexibility in controlling the tradeoff between false-negatives and false-positives  In this subsection, we explore the effect of hyperparameters (i e , $\alpha $ and $\beta $) in TI to test how they manipulate the tradeoff  We conduct experiments on the Chinese OntoNotes4 0 NER dataset and English QuoRef MRC dataset to examine the influence of tradeoff between precision and recall  Experiment results are shown in Table   The highest F1 for Chinese OntoNotes4 0 is 84 67 when $\alpha $ is set to 0 6 while for QuoRef, the highest F1 is 68 44 when $\alpha $ is set to 0 4  In addition, we can observe that the performance varies a lot as $\alpha $ changes in distinct datasets, which shows that the hyperparameters $\alpha ,\beta $ play an important role in the proposed method 
Conclusion
In this paper, we alleviate the severe data imbalance issue in NLP tasks  We propose to use dice loss in replacement of the standard cross-entropy loss, which performs as a soft version of F1 score  Using dice loss can help narrow the gap between training objectives and evaluation metrics  Empirically, we show that the proposed training objective leads to significant performance boost for part-of-speech, named entity recognition, machine reading comprehension and paraphrase identification tasks 
Context 3: 
When the bot chats with multiple clients, the predicates are stored relative to each client ID  For example, the markup INLINEFORM0 set name INLINEFORM1 "name" INLINEFORM2 Matthew INLINEFORM3 /set INLINEFORM4 stores the string Matthew under the predicate named "name"  Subsequent activations of INLINEFORM5 get name="name" INLINEFORM6 return "Matthew"  In addition, one of the simple tricks that makes ELIZA and A L I C E  so believable is a pronoun swapping substitution  For instance:
U: My husband would like to invest with me 
S: Who else in your family would like to invest with you?
Types of Intentions
According to the types of intentions, conversational systems can be classified into two categories: a) goal-driven or task oriented, and b) non-goal-driven or end-to-end systems 
In a goal-driven system, the main objective is to interact with the user so that back-end tasks, which are application specific, are executed by a supporting system  As an example of application we can cite technical support systems, for instance air ticket booking systems, where the conversation system must interact with the user until all the required information is known, such as origin, destination, departure date and return date, and the supporting system must book the ticket  The most widely used approaches for developing these systems are Partially-observed Decision Processes (POMDP) BIBREF30 , Hidden Markov Models (HMM) BIBREF29 , and more recently, Memory Networks BIBREF28   Given that these approaches are data-oriented, a major issue is to collect a large corpora of annotated task-specific dialogs  For this reason, it is not trivial to transfer the knowledge from one to domain to another  In addition, it might be difficult to scale up to larger sets of tasks 
Non-goal-driven systems (also sometimes called reactive systems), on the other hand, generate utterances in accordance to user input, e g  language learning tools or computer games characters  These systems have become more popular in recent years, mainly owning to the increase of popularity of Neural Networks, which is also a data-oriented approach  The most recent state of the art to develop such systems have employed Recurrent Neural Networs (RNN) BIBREF34 , Dynamic Context-Sensitive Generation BIBREF35 , and Memory Networks BIBREF36 , just to name a few  Nevertheless, probabilistic methods such as Hidden Topic Markov Models (HTMM) BIBREF37 have also been evaluated  Goal-driven approach can create both pro-active and reactive chatbots, while non-goal-driven approach creates reactive chatbots  In addition, they can serve as a tool to goal-driven systems as in BIBREF28   That is, when trained on corpora of a goal-driven system, non-goal-driven systems can be used to simulate user interaction to then train goal-driven models 
Types of Context Reasoning
A dialogue system may support the context reasoning or not  Context reasoning is necessary in many occasions  For instance, when partial information is provided the chatbot needs to be able to interact one or more turns in order to get the complete information in order to be able to properly answer  In BIBREF38 , the authors present a taxonomy of errors in conversational systems  The ones regarding context-level errors are the ones that are perceived as the top-10 confusing and they are mainly divided into the following:
Excess/lack of proposition: the utterance does not provide any new proposition to the discourse context or provides excessive information than required 
Contradiction: the utterance contains propositions that contradict what has been said by the system or by the user 
Non-relevant topic: the topic of the utterance is irrelevant to the current context such as when the system suddenly jumps to some other topic triggered by some particular word in the previous user utterance 
Unclear relation: although the utterance might relate to the previous user utterance, its relation to the current topic is unclear 
Topic switch error: the utterance displays the fact that the system missed the switch in topic by the user, continuing with the previous topic 
Rule-oriented
In the state of the art most of the proposed approaches for context reasoning lies on rules using logics and knowledge bases as described in the Rule-oriented architecture sub-section  Given a set of facts extracted from the dialogue history and encoded in, for instance, FOL statements, a queries can be posed to the inference engine and produce answers  For instance, see the example in Table TABREF37   The sentences were extracted from BIBREF36 (which does not use a rule-oriented approach), and the first five statements are their respective facts  The system then apply context reasoning for the query Q: Where is the apple 
If statements above are received on the order present in Table TABREF37 , if the query Q: Where is the apple is sent, the inference engine will produce the answer A: Bedroom (i e , the statement INLINEFORM0 is found by the model and returned as True) 
Nowadays, the most common way to store knowledge bases is on triple stores, or RDF (Resource Description Framework) stores  A triple store is a knowledge base for the storage and retrieval of triples through semantic queries  A triple is a data entity composed of subject-predicate-object, like "Sam is at the kitchen" or "The apple is with Sam", for instance  A query language is needed for storing and retrieving data from a triple store  While SPARQL is a RDF query language, Rya is an open source scalable RDF triple store built on top of Apache Accumulo  Originally developed by the Laboratory for Telecommunication Sciences and US Naval Academy, Rya is currently being used by a number of american government agencies for storing, inferencing, and querying large amounts of RDF data 
A SPARQL query has a SQL-like syntax for finding triples matching specific patterns  For instance, see the query below  It retrieves all the people that works at IBM and lives in New York:
SELECT ?people
WHERE {
?people <worksAt> <IBM>  
?people <livesIn> <New York> 
}
Since triple stores can become huge, Rya provides three triple table index BIBREF39 to help speeding up queries:
SPO: subject, predicate, object
POS: predicate, object, subject
OSP: object, subject, predicate
While Rya is an example of an optimized triple store, a rule-oriented chatbot can make use of Rya or any triple store and can call the semantic search engine in order to inference and generate proper answers 
Data-oriented
Recent papers have used neural networks to predict the next utterance on non-goal-driven systems considering the context, for instance with Memory Networks BIBREF40   In this work BIBREF36 , for example the authors were able to generate answers for dialogue like below:
Sam walks into the kitchen 
Sam picks up an apple 
Sam walks into the bedroom 
Sam drops the apple 
Q: Where is the apple?
A: Bedroom
Sukhbaatar's model represents the sentence as a vector in a way that the order of the words matter, and the model encodes the temporal context enhancing the memory vector with a matrix that contains the temporal information  During the execution phase, Sukhbaatar's model takes a discrete set of inputs INLINEFORM0 that are to be stored in the memory, a query INLINEFORM1 , and outputs an answer INLINEFORM2   Each of the INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 contains symbols coming from a dictionary with INLINEFORM6 words  The model writes all INLINEFORM7 to the memory up to a fixed buffer size, and then finds a continuous representation for the INLINEFORM8 and INLINEFORM9   The continuous representation is then processed via multiple computational steps to output INLINEFORM10   This allows back propagation of the error signal through multiple memory accesses back to the input during training  Sukhbaatar's also presents the state of the art of recent efforts that have explored ways to capture dialogue context, treated as long-term structure within sequences, using RNNs or LSTM-based models  The problem of this approach is that it is has not been tested for goal-oriented systems  In addition, it works with a set of sentences but not necessary from multi-party bots 
Platforms
Regarding current platforms to support the development of conversational systems, we can categorize them into three types: platforms for plugging chatbots, for creating chatbots and for creating service chatbots  The platforms for plugging chatbots provide tools for integrating them another system, like Slack  The chatbots need to receive and send messages in a specific way, which depends on the API and there is no support for actually helping on building chatbots behavior with natural language understanding  The platforms for creating chatbots mainly provide tools for adding and training intentions together with dialogue flow specification and some entities extraction, with no reasoning support  Once the models are trained and the dialogue flow specified, the chatbots are able to reply to the received intention  The platforms for creating service chatbots provide the same functionalities as the last one and also provide support for defining actions to be executed by the chatbots when they are answering to an utterance  Table TABREF43 summarizes current platforms on the market accordingly to these categories  There is a lack on platforms that allow to create chatbots that can be coordinated in a multiparty chat with governance or mediation 
A Conceptual Architecture for Multiparty-Aware Chatbots
In this section the conceptual architecture for creating a hybrid rule and machine learning-based MPCS is presented  The MPCS is defined by the the entities and relationships illustrated in Fig  FIGREF44 which represents the chatbot's knowledge  A Chat Group contains several Members that join the group with a Role  The role may constrain the behavior of the member in the group  Chatbot is a type of Role, to differentiate from persons that may also join with different roles  For instance, a person may assume the role of the owner of the group, or someone that was invited by the owner, or a domain role like an expert, teacher or other 
When a Member joins the Chat Group, it/he/she can send Utterances  The Member then classifies each Utterance with an Intent which has a Speech Act  The Intent class, Speech Act class and the Intent Flow trigger the Action class to be executed by the Member that is a Chatbot  The Chatbots associated to the Intention are the only ones that know how to answer to it by executing Actions  The Action, which implements one Speech Act, produces answers which are Utterances, so, for instance, the Get_News action produces an Utterance for which Intention's speech act is Inform_News  The Intent Flow holds the intent's class conversation graph which maps the dialog state as a decision tree  The answer's intention class is mapped in the Intent Flow as a directed graph G defined as following: DISPLAYFORM0
From the graph definitions, INLINEFORM0 is for vertices and INLINEFORM1 is for relations, which are the arrows in the graph  And in Equation EQREF46 :
INLINEFORM0 is the set of intentions pairs,
INLINEFORM0 is the set of paths to navigate through the intentions,
INLINEFORM0 is the arrow's head, and
INLINEFORM0 is the arrow's tail 
This arrow represents a turn from an utterance with INLINEFORM0 intention class which is replying to an utterance with INLINEFORM1 intention class to the state which an utterance with INLINEFORM2 intention's class is sent 
INLINEFORM0 is the intention class of the answer to be provided to the received INLINEFORM1 intention class 
In addition, each intent's class may refer to many Entities which, in turn, may be associated to several Features  For instance, the utterance
"I would like to invest USD10,000 in Savings Account for 2 years"
contains one entity – the Savings Account's investment option – and two features – money (USD10,000) and period of time (2 years)  The Intent Flow may need this information to choose the next node which will give the next answer  Therefore, if the example is changed a little, like
"I would like to invest in Savings Account",
INLINEFORM0 is constrained by the "Savings Account" entity which requires the two aforementioned features  Hence, a possible answer by one Member of the group would be
"Sure, I can simulate for you, what would be the initial amount and the period of time of the investment?"
With these conceptual model's elements, a MPCS system can be built with multiple chatbots  Next subsection further describes the components workflow 
Workflow
Figure FIGREF48 illustrates from the moment that an utterance is sent in a chat group to the moment a reply is generated in the same chat group, if the case  One or more person may be in the chat, while one or more chatbots too  There is a Hub that is responsible for broadcasting the messages to every Member in the group, if the case  The flow starts when a Member sends the utterance which goes to the Hub and, if allowed, is broadcasted  Many or none interactions norms can be enforced at this level depending on the application  Herein, a norm can be a prohibition, obligation or permission to send an utterance in the chat group 
Once the utterance is broadcasted, a chatbot needs to handle the utterance  In order to properly handle it, the chatbot parses the utterance with several parsers in the Parsing phase: a Topic Classifier, the Dependency Parsing, which includes Part-of-Speech tags and semantics tags, and any other that can extract metadata from the utterance useful for the reasoning  All these metadata, together with more criteria, may be used in the Frame parsing which is useful for context reasoning  All knowledge generated in this phase can be stored in the Context  Then, the Intent Classifier tries to detect the intent class of the utterance  If detected, the Speech Act is also retrieved  And an Event Detector can also check if there is any dialog inconsistency during this phase 
After that, the Filtering phase receives the object containing the utterance, the detected intent, and all metadata extracted so far and decides if an action should be performed to reply to the utterance  If yes, it is sent to the Acting phase which performs several steps  First the Action Classifier tries to detect the action to be performed  If detected, the action is executed  At this step, many substeps may be performed, like searching for an information, computing maths, or generating information to create the answer  All of this may require a search in the Context and also may activate the Error Detector component to check if the dialog did not run into a wrong state  After the answer is generated, the Filtering phase is activated again to check if the reply should be really sent  If so, it is sent to the Hub which, again may check if it can be broadcasted before actually doing it 
The topic classifier is domain-dependent and is not mandatory  However, the chatbot can better react when the intent or action is not detected, which means that it does not know how to answer  Many reasons might explain this situation: the set of intents might be incomplete, the action might not have produced the proper behavior, misunderstanding might happen, or the chatbot was not designed to reply to a particular topic  In all cases, it must be able to produce a proper reply, if needed  Because this might happen throughout the workflow, the sooner that information is available, the better the chatbot reacts  Therefore it is one of the first executions of the flow 
Dependency is the notion that linguistic units, e g  words, are connected to each other by directed links  The (finite) verb is taken to be the structural center of clause structure  All other syntactic units (words) are either directly or indirectly connected to the verb in terms of the directed links, which are called dependencies  It is a one-to-one correspondence: for every element (e g  word or morph) in the sentence, there is exactly one node in the structure of that sentence that corresponds to that element  The result of this one-to-one correspondence is that dependency grammars are word (or morph) grammars  All that exist are the elements and the dependencies that connect the elements into a structure  Dependency grammar (DG) is a class of modern syntactic theories that are all based on the dependency relation 
Semantic dependencies are understood in terms of predicates and their arguments  Morphological dependencies obtain between words or parts of words  To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, a tagset that consists of twelve universal part-of-speech categories was proposed BIBREF41  
Dependency parsers have to cope with a high degree of ambiguity and nondeterminism which let to different techniques than the ones used for parsing well-defined formal languages  Currently the mainstream approach uses algorithms that derive a potentially very large set of analyses in parallel and when disambiguation is required, this approach can be coupled with a statistical model for parse selection that ranks competing analyses with respect to plausibility BIBREF42  
Below we present an example of a dependency tree for the utterance:
"I want to invest 10 thousands":
[s]""blue[l]:black
"tree": {
"want VERB ROOT": {
"I PRON nsubj": {},
"to ADP mark": {},
"invest VERB nmod": {
"thousands NOUN nmod": {
"10 NUM nummod": {}
}
}
}
The coarse-grained part-of-speech tags, or morphological dependencies (VERB, PRON, ADP, NOUN and NUM) encode basic grammatical categories and the grammatical relationships (nsubjs, nmod, nummod) are defined in the Universal Dependencies project BIBREF41  
In this module, the dependency tree generated is used together with a set of rules to extract information that is saved in the context using the Frame-based approach  This approach fills the slots of the frame with the extracted values from the dialogue  Frames are like forms and slots are like fields  Using the knowledge's conceptual model, the fields are represented by the elements Entities and Features  In the dependency tree example, the entity would be the implicit concept: the investment option, and the feature is the implicit concept: initial amount – 10 thousands  Since the goal is to invest, and there are more entities needed for that (i e , fields to be filled), the next node in the Intention Flow tree would return an utterance which asks the user the time of investment, if he/she has not provided yet 
This module could be implemented using different approaches according to the domain, but tree search algorithms will be necessary for doing the tree parsing 
The Intent Classifier component aims at recognizing not only the Intent but the goal of the utterance sent by a Member, so it can properly react  The development of an intent classifier needs to deal with the following steps:
i) the creation of dataset of intents, to train the classification algorithm;
ii) the design of a classification algorithm that provides a reasonable level of accuracy;
iii) the creation of dataset of trees of intents, the same as defined in i) and which maps the goals;
iv) the design of a plan-graph search algorithm that maps the goal's state to a node in the graph;
There are several approaches to create training sets for dialogues: from an incremental approach to crowdsourcing  In the incremental approach, the Wizard of Oz method can be applied to a set of potential users of the system, and from this study, a set of questions that the users asked posted to the `fake' system can be collected  These questions have to be manually classified into a set of intent classes, and used to train the first version of the system  Next, this set has to be increased both in terms of number of classes and samples per class 
The Speech Act Classifier can be implemented with many speech act classes as needed by the application  The more classes, the more flexible the chatbot is  It can be built based on dictionaries, or a machine learning-based classifier can be trained  In the table below we present the main and more general speech act classes BIBREF43 used in the Chatbots with examples to differentiate one from another:
There are at least as many Action classes as Speech Act classes, since the action is the realization of a speech act  The domain specific classes, like "Inform_News" or "Inform_Factoids", enhance the capabilities of answering of a chatbot 
The Action Classifier can be defined as a multi-class classifier with the tuple DISPLAYFORM0
where INLINEFORM0 is the intent of the answer defined in ( EQREF46 ), INLINEFORM1 is the speech act of the answer, INLINEFORM2 and INLINEFORM3 are the sets of entities and features needed to produce the answer, if needed, respectively 
This component is responsible for implementing the behavior of the Action class  Basic behaviors may exist and be shared among different chatbots, like the ones that implement the greetings, thanks or not understood  Although they can be generic, they can also be personalized to differentiate the bot from one another and also to make it more "real"  Other cases like to inform, to send a query, to send a proposal, they are all domain-dependent and may require specific implementations 
Anyway, figure FIGREF59 shows at the high level the generic workflow  If action class detected is task-oriented, the system will implement the execution of the task, say to guide a car, to move a robot's arm, or to compute the return of investments  The execution might need to access an external service in the Internet in order to complete the task, like getting the inflation rate, or the interest rate, or to get information about the environment, or any external factor  During the execution or after it is finished, the utterance is generated as a reply and, if no more tasks are needed, the action execution is finished 
In the case of coordination of chatbots, one or more chatbots with the role of mediator may exist in the chat group and, at this step, it is able to invite one or more chatbots to the chat group and it is also able to redirect the utterances, if the case 
The proposed architecture addresses the challenges as the following:
What is the message/utterance about? solved by the Parsing phase;
Who should reply to the utterance? solved by the Filtering phase and may be enforced by the Hub;
How the reply should be built/generated? solved by the Acting phase;
When should the reply be sent? may be solved by the Acting phase or the Filtering phase, and may be enforced by the Hub;
And Context and Logging module is used throughout all phases 
Architecture Implementation and Evaluation
This section presents one implementation of the conceptual architecture presented in last section  After many refactorings, a framework called SABIA (Speech-Act-Based Intelligent Agents Framework) has been developed and CognIA (Cognitive Investment Advisor) application has been developed as an instantiation of SABIA framework  We present then the accuracy and some automated tests of this implementation 
Speech-Act-based Intelligent Agents Framework
SABIA was developed on top of Akka middleware  Akka is a toolkit and runtime that implements the Actor Model on the JVM  Akka's features, like concurrency, distributed computing, resilience, and message-passing were inspired by Erlang's actor model BIBREF44 BIBREF45   The actor model is a mathematical model of concurrent computation that treats "actors" as the universal primitives of concurrent computation  In response to a message that it receives, an actor can: make local decisions, create more actors, send more messages, and determine how to respond to the next received message  Actors may modify private state, but can only affect each other through messages (avoiding the need for any locks)  Akka middleware manages the actors life cycle and actors look up by theirs name, locally or remotely 
We implemented each Member of the Chat Group as an Actor by extending the UntypedActor class of Akka middleware  Yet, we created and implemented the SabiaActorSystem as a singleton (i e , a single instance of it exists in the system) BIBREF46 that has a reference to Akka's ActorSystem  During SabiaActorSystem's initialization, all parsers that consume too much memory during their initialization to load models are instantiated as singletons  In this way, we save time on their calls during the runtime  Moreover, all chat group management, like to join or leave the group, or to broadcast or filter a message at the Hub level is implemented in SABIA through the Chat Group behavior 
This is implemented in SABIA as a singleton that is initialized during the SabiaActorSystem initialization with the URL of the service that implements the dependency parsing and is used on each utterance's arrival through the execution of the tagUtterance method  The service must retrieve a JSON Object with the dependency tree which is then parsed using depth-first search 
SABIA does not support invariants for frame parsing  We are leaving this task to the instantiated application 
There are two intent classifiers that can be loaded with trained models in order to be ready to be used at runtime: the 1-nearest-neighbor (1NN) and the SVM-based classifier 
SABIA implements the Action Classifier assuming that the application uses a relational database with a data schema that implements the conceptual model presented in Figure FIGREF44   Then the invariants parts that use SQL are already present and the application only needs to implement the database connection and follow the required data schema 
SABIA provides partial implemented behavior for the Action through the Template method design pattern BIBREF46 , which implements the invariants parts of the action execution and leaves placeholders for customization 
CognIA: A Cognitive Investment Advisor
We developed CognIA, which is an instantiation of Sabia framework  A conversation is composed of a group chat that can contain multiple users and multiple chatbots  This example, in particular, has a mediator that can help users on financial matters, more specifically on investment options  For example, consider the following dialogue in the table below:
The Table TABREF71 shows an example that uses the mixed-initiative dialogue strategy, and a dialogue mediator to provide coordination control  In this example of an application, there are many types of intentions that should be answered: Q&A (question and answer) about definitions, investment options, and about the current finance indexes, simulation of investments, which is task-oriented and requires computation, and opinions, which can be highly subjective 
In Table SECREF72 , we present the interaction norms that were needed in Cognia  The Trigger column describes the event that triggers the Behavior specified in the third column  The Pre-Conditions column specifies what must happen in order to start the behavior execution  So, for instance, line 2, when the user sends an utterance in the chat group, an event is triggered and, if the utterance's topic is CDB (Certificate of Deposit which is a fixed rate investment) or if it is about the Savings Account investment option and the speech act is not Query_Calculation and the CDB and Savings Account members are not in the chat, then the behavior is activated  The bot members that implement these behaviors are called cdbguru and poupancaguru  Therefore these names are used when there is a mention 
Note that these interactions norms are not explicitly defined as obligations, permissions, and prohibitions  They are implict from the behavior described  During this implementation, we did not worry about explicitly defining the norms, because the goal was to evaluate the overall architecture, not to enhance the state of the art on norms specification for conversational systems  In addition, CognIA has only the presented interaction norms defined in Table SECREF72 , which is a very small set that that does not required model checking or verification of conflicts 
|p2cm|p5 0cm|p5 0cm|Cognia Interaction NormsCognia Interaction Norms
Trigger Pre-Conditions Behavior
On group chat creation Cognia chatbot is available Cognia chatbot joins the chat with the mediator role and user joins the chat with the owner_user role
On utterance sent by user Utterance's topic is CDB (cdbguru) or Savings Account (poupancaguru) and speech act is not Query_Calculation and they are not in the chat Cognia invites experts to the chat and repeats the utterance to them
On utterance sent by user Utterance's topic is CDB (cdbguru) or Savings Account (poupancaguru) and speech act is not Query_Calculation and they are in the chat Cognia waits for while and cdbguru or poupancaguru respectively handles the utterance  If they don't understand, they don't reply
On utterance sent by the experts If Cognia is waiting for them and has received both replies Cognia does not wait anymore
On utterance sent Utterance mentions cdbguru or poupancaguru cdbguru or poupancaguru respectively handles the utterance
On utterance sent Utterance mentions cdbguru or poupancaguru and they don't reply after a while and speech act is Query_Calculation Cognia sends I can only chat about investments   
On utterance sent Utterance mentions cdbguru or poupancaguru and they don't reply after while and speech act is not Query_Calculation Cognia sends I didn't understand
On utterance sent Utterance's speech act is Query_Calculation and period or initial amount of investment were not specified Cognia asks the user the missing information
On utterance sent Utterance's speech act is Query_Calculation and period and initial amount of investment were specified and the experts are not in the chat Cognia invites experts to the chat and repeats the utterance to them
On utterance sent Utterance's speech act is Query_Calculation and period and initial amount of investment were specified and the experts are in the chat Cognia repeats the utterance to experts
On utterance sent Utterance's speech act is Query_Calculation Cognia extracts variables and saves the context
On utterance sent Utterance's speech act is Query_Calculation and the experts are in the chat and the experts are mentioned Experts extract information, save in the context, compute calculation and send information
On utterance sent Utterance's speech act is Inform_Calculation and Cognia received all replies Cognia compares the results and inform comparison
On utterance sent Utterance mentions a chatbot but has no other text The chatbot replies How can I help you?
On utterance sent Utterance is not understood and speech act is Question The chatbot replies I don't know    I can only talk about topic X
On utterance sent Utterance is not understood and speech act is not Question The chatbot replies I didn't understand
On utterance sent Utterance's speech act is one of { Greetings, Thank, Bye } All chatbots reply to utterance
On group chat end All chatbots leave the chat, and the date and time of the end of chat is registered
We instantiated SABIA to develop CognIA as follows: the Mediator, Savings Account, CDB and User Actors are the Members of the Chat Group  The Hub was implemented using two servers: Socket io and Node JS which is a socket client of the Socket io server  The CognIA system has also one Socket Client for receiving the broadcast and forwarding to the Group Chat Manager  The former will actually do the broadcast to every member after enforcing the norms that applies specified in Table SECREF72   Each Member will behave according to this table too  For each user of the chat group, on a mobile or a desktop, there is its corresponding actor represented by the User Actor in the figure

ground_truth: Using DSC loss improves the F1 score by +0.58 for MRPC and +0.73 for QQP

+0.58

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What datasets are used?
Context 3:  Its main job is to receive Akka's broadcast and forward to the Socket io server, so it can be finally propagated to the users 
All the intents, actions, factual answers, context and logging data are saved in DashDB (a relational Database-as-a-Service system)  When an answer is not retrieved, a service which executes the module Search Finance on Social Media on a separate server is called  This service was implemented with the assumption that finance experts post relevant questions and answers on social media  Further details are explained in the Action execution sub-section 
We built a small dictionary-based topic classifier to identify if an utterance refers to finance or not, and if it refers to the two investment options (CDB or Savings Account) or not 
The dependency parsing is extremely important for computing the return of investment when the user sends an utterance with this intention  Our first implementation used regular expressions which led to a very fragile approach  Then we used a TensorFlow implementation BIBREF47 of a SyntaxNet model for Portuguese and used it to generate the dependency parse trees of the utterances  The SyntaxNet model is a feed-forward neural network that operates on a task-specific transition system and achieves the state-of-the-art on part-of-speech tagging, dependency parsing and sentence compression results BIBREF48   Below we present output of the service for the utterance:
"I want to invest 10 thousands in 40 months":
[s]""blue[l]:black
{ "original": "I would like to invest 10 thousands in 40 months",
"start_pos": [
23,
32],
"end_pos": [
27,
33],
"digits": [
10000,
40],
"converted": "I would like to invest 10000 in 40 months",
"tree": {
"like VERB ROOT": {
"I PRON nsubj": {},
"would MD aux":{
"invest VERB xcomp":{
"to TO aux": {},
"10000 NUM dobj": {},
"in IN prep": {
"months NOUN pobj":{
"40 NUM num": {}}}}}}}
The service returns a JSON Object containing six fields: original, start_pos, end_pos, digits, converted and tree  The original field contains the original utterance sent to the service  The converted field contains the utterance replaced with decimal numbers, if the case (for instance, "10 thousands" was converted to "10000" and replaced in the utterance)  The start_pos and end_pos are arrays that contain the start and end char positions of the numbers in the converted utterance  While the tree contains the dependency parse tree for the converted utterance 
Given the dependency tree, we implemented the frame parsing which first extracts the entities and features from the utterance and saves them in the context  Then, it replaces the extracted entities and features for reserved characters 
extract_period_of_investment (utteranceTree) [1] [t] numbersNodes INLINEFORM0 utteranceTree getNumbersNodes(); [t] foreach(numberNode in numbersNodes) do [t] parentsOfNumbersNode INLINEFORM1 numbersNode getParents() [t] foreach(parent in parentsOfNumbersNodes) do [t] if ( parent name contains { "day", "month", "year"} ) then [t] parentOfParent INLINEFORM2 parent getParent() [t] if ( parentOfParent is not null and
parentOfParent getPosTag==Verb and
parentOfParent name in investmentVerbsSet ) then [t] return numberNode
Therefore an utterance like "I would like to invest 10 thousands in 3 years" becomes "I would like to invest #v in #dt years"  Or "10 in 3 years" becomes "#v in #dt years", and both intents have the same intent class 
For doing that we implemented a few rules using a depth-first search algorithm combined with the rules as described in Algorithm UID79 , Algorithm UID79 and Algorithm UID79   Note that our parser works only for short texts on which the user's utterance mentions only one period of time and/ or initial amount of investment in the same utterance 
extract_initial_amount_of_investment (utteranceTree) [1] [t] numbersNodes INLINEFORM0 utteranceTree getNumbersNodes(); [t] foreach(numberNode in numbersNodes) do [t] parentsOfNumbersNode INLINEFORM1 numbersNode getParents() [t] foreach(parent in parentsOfNumbersNodes) do [t] if ( parent name does not contain { "day", "month", "year"} ) then [t] return numberNode
frame_parsing(utterance, utteranceTree) [1] [t] period INLINEFORM0 extract_period_of_investment (utteranceTree) [t] save_period_of_investment(period) [t] value INLINEFORM1 extract_initial_amount_of_investment (utteranceTree) [t] save_initial_amount_of_investment(value) [t] new_intent INLINEFORM2 replace(new_intent, period, "#dt") [t] new_intent INLINEFORM3 replace(new_intent, value, "#v")
In CognIA we have complemented the speech act classes with the ones related to the execution of specific actions  Therefore, if the chatbot needed to compute the return of investment, then, once it is computed, the speech act of the reply will be Inform_Calculation and the one that represents the query for that is Query_Calculation  In table TABREF81 we list the specific ones 
Given that there is no public dataset available with financial intents in Portuguese, we have employed the incremental approach to create our own training set for the Intent Classifier  First, we applied the Wizard of Oz method and from this study, we have collected a set of 124 questions that the users asked  Next, after these questions have been manually classified into a set of intent classes, and used to train the first version of the system, this set has been increased both in terms of number of classes and samples per class, resulting in a training set with 37 classes of intents, and a total 415 samples, with samples per class ranging from 3 to 37 
We have defined our classification method based on features extracted from word vectors  Word vectors consist of a way to encode the semantic meaning of the words, based on their frequency of co-occurrence  To create domain-specific word vectors, a set of thousand documents are needed related to desired domain  Then each intent from the training set needs to be encoded with its corresponding mean word vector  The mean word vector is then used as feature vector for standard classifiers 
We have created domain-specific word vectors by considering a set 246,945 documents, corresponding to of 184,001 Twitter posts and and 62,949 news articles, all related to finance  
The set of tweets has been crawled from the feeds of blog users who are considered experts in the finance domain  The news article have been extracted from links included in these tweets  This set contained a total of 63,270,124 word occurrences, with a vocabulary of 97,616 distinct words  With the aforementioned word vectors, each intent from the training set has been encoded with its corresponding mean word vector  The mean word vector has been then used as feature vector for standard classifiers 
As the base classifier, we have pursued with a two-step approach  In the first step, the main goal was to make use of a classifier that could be easily retrained to include new classes and intents  For this reason, the first implementation of the system considered an 1-nearest-neighbor (1NN) classifier, which is simply a K-nearest-neighbor classifier with K set to 1  With 1NN, the developer of the system could simply add new intents and classes to the classifier, by means of inserting new lines into the database storing the training set  Once we have considered that the training set was stable enough for the system, we moved the focus to an approach that would be able to provide higher accuracy rates than 1NN  For this, we have employed Support Vector Machines (SVM) with a Gaussian kernel, the parameters of which are optimized by means of a grid search 
We manually mapped the intent classes used to train the intent classifier to action classes and the dependent entities and features, when the case  Table TABREF85 summarizes the number of intent classes per action class that we used in CognIA 
For the majority of action classes we used SABIA's default behavior  For instance, Greet and Bye actions classes are implemented using rapport, which means that if the user says "Hi" the chatbot will reply "Hi" 
The Search News, Compute and Ask More classes are the ones that require specific implemention for CognIA as following:
Search News: search finance on social media service BIBREF49 , BIBREF50 receives the utterance as input, searches on previously indexed Twitter data for finance for Portuguese and return to the one which has the highest score, if found 
Ask More: If the user sends an utterance that has the intention class of simulating the return of investment, while not all variables to compute the return of investment are extracted from the dialogue, the mediator keeps asking the user these information before it actually redirects the query to the experts  This action then checks the state of the context given the specified intent flow as described in ( EQREF46 ) and ( EQREF57 ) in section SECREF4 to decide which variables are missing  For CognIA we manually added these dependencies on the database 
Compute: Each expert Chatbot implements this action according to its expertise  The savings account chatbot computes the formula ( EQREF90 ) and the certificate of deposit computes the formula ( EQREF92 )  Both are currently formulas for estimating in Brazil  DISPLAYFORM0
where INLINEFORM0 is the return of investment for the savings account, INLINEFORM1 is the initial value of investment, INLINEFORM2 is the savings account interest rate and INLINEFORM3 is the savings account rate base  DISPLAYFORM0
where INLINEFORM0 is the return of investment for certificate of deposit, INLINEFORM1 is the initial value of investment, INLINEFORM2 is the Interbank Deposit rate (DI in Portuguese), INLINEFORM3 is the ID's percentual payed by the bank (varies from 90% to 120%), INLINEFORM4 is the number of days the money is invested, and finally INLINEFORM5 is the income tax on the earnings 
Intention Classifier Accuracy
In Table TABREF95 we present the comparison of some distinct classification on the first version of the training set, i e  the set used to deploy the first classifier into the system  Roughly speaking, the 1NN classifier has been able to achieve a level of accuracy that is higher than other well-known classifiers, such as Logistic Regression and Naïve Bayes, showing that 1NN is suitable as a development classifier  Nevertheless, a SVM can perform considerable better than 1NN, reaching accuracies of about 12 percentage points higher, which demonstrates that this type of base classifier is a better choice to be deployed once the system is stable enough  It is worth mentioning that these results consider the leave-one-out validation procedure, given the very low number of samples in some classes 
As we mentioned, the use of an 1NN classifier has allowed the developer of the system to easily add new intent classes and samples whenever they judged it necessary, so that the system could present new actions, or the understanding of the intents could be improved  As a consequence, the initial training set grew from 37 to 63 classes, and from 415 to 659 samples, with the number of samples per class varying from 2 to 63  For visualizing the impact on the accuracy of the system, in Table TABREF96 we present the accuracy of the same classifiers used in the previous evaluation, in the new set  In this case, we observe some drop in accuracy for 1NN, showing that this classifier suffers in dealing with scalability  On the other hand, SVM has shown to scale very well to more classes and samples, since its accuracy kept at a very similar level than that with the other set, with a difference of about only 1 percentage point 
Testing SABIA
In this section, we describe the validation framework that we created for integration tests  For this, we developed it as a new component of SABIA's system architecture and it provides a high level language which is able to specify interaction scenarios that simulate users interacting with the deployed chatbots  The system testers provide a set of utterances and their corresponding expected responses, and the framework automatically simulates users interacting with the bots and collect metrics, such as time taken to answer an utterance and other resource consumption metrics (e g , memory, CPU, network bandwidth)  Our goal was to: (i) provide a tool for integration tests, (ii) to validate CognIA's implementation, and (iii) to support the system developers in understanding the behavior of the system and which aspects can be improved  Thus, whenever developers modify the system's source code, the modifications must first pass the automatic test before actual deployment 
The test framework works as follows  The system testers provide a set INLINEFORM0 of dialogues as input  Each dialogue INLINEFORM1 INLINEFORM2 INLINEFORM3 is an ordered set whose elements are represented by INLINEFORM4 , where INLINEFORM5 is the user utterance and INLINEFORM6 is an ordered set of pairs INLINEFORM7 that lists each response INLINEFORM8 each chatbot INLINEFORM9 should respond when the user says INLINEFORM10   For instance, Table UID98 shows a typical dialogue ( INLINEFORM11 ) between a user and the CognIA system  Note that we are omitting part of the expected answer with "   " just to better visualize the content of the table 
|p3 6cmp0 4cmp4 5cmp3 2cm|Content of dialogue INLINEFORM0 (example of dialogue in CognIA)Content of dialogue INLINEFORM1 (example of dialogue in CognIA
User utterance INLINEFORM0 rId Expected response INLINEFORM1 Chatbot INLINEFORM2
gray!25 hello 1 Hello Mediator
white what is cdb? 2 @CDBExpert what is cdb? Mediator
white 3 CDB is a type of investment that    CDB Expert
gray!25 which is better: cdb or savings account? 4 I found a post in the social media for     Mediator
white i would like to invest R$ 50 in six months 5 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
white 6 If you invest in Savings Account,     Savings Account Exp 
white 7 If you invest in CDB,    CDB Expert
white 8 Thanks Mediator
white 9 @User, there is no significant difference   Mediator
gray!25 so i want to invest R$ 10000 in 2 years 10 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
gray!25 11 If you invest in Savings Account,    Savings Account Exp 
gray!25 12 If you invest in CDB,    CDB Expert
gray!25 13 Thanks Mediator
gray!25 14 @User, in that case, it is better    Mediator
white what if i invest R$10,000 in 5 years? 15 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
white 16 If you invest in Saving Account,    Savings Account Exp 
white 17 If you invest in CDB,    CDB Expert
white 18 Thanks Mediator
white 19 @User, in that case, it is better    Mediator
gray!25 how about 15 years? 20 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
gray!25 21 If you invest in Savings Account,    Savings Account Exp
gray!25 22 If you invest in CDB,    CDB Expert
gray!25 23 Thanks Mediator
gray!25 24 @User, in that case, it is better    Mediator
white and 50,0000? 25 @SavingsAccountExpert and @CDBExpert, could you do a simulation    Mediator
white 26 If you invest in Savings Account,    Savings Account Exp 
white 27 If you invest in CDB,    CDB Expert
white 28 Thanks Mediator
white 29 @User, in that case, it is better   Mediator
gray!25 I want to invest in 50,000 for 15 years in CDB 30 Sure, follow this link to your bank    Mediator
white thanks 31 You are welcome  Mediator
The testers may also inform the number of simulated users that will concurrently use the platform  Then, for each simulated user, the test framework iterates over the dialogues in INLINEFORM0 and iterates over the elements in each dialogue to check if each utterance INLINEFORM1 was correctly responded with INLINEFORM2 by the chatbot INLINEFORM3   There is a maximum time to wait  If a bot does not respond with the expected response in the maximum time (defined by the system developers), an error is raised and the test is stopped to inform the developers about the error  Otherwise, for each correct bot response, the test framework collects the time taken to respond that specific utterance by the bot for that specific user and continues for the next user utterance  Other consumption resource metrics (memory, CPU, network, disk)  The framework is divided into two parts  One part is responsible to gather resource consumption metrics and it resides inside SABIA  The other part works as clients (users) interacting with the server  It collects information about time taken to answer utterances and checks if the utterances are answered correctly 
By doing this, we not only provide a sanity test for the domain application (CognIA) developed in SABIA framework, but also a performance analysis of the platform  That is, we can: validate if the bots are answering correctly given a pre-defined set of known dialogues, check if they are answering in a reasonable time, and verify the amount of computing resources that were consumed to answer a specific utterance  Given the complexity of CognIA, these tests enable debugging of specific features like: understanding the amount of network bandwidth to use external services, or analyzing CPU and memory consumption when responding a specific utterance  The later may happen when the system is performing more complex calculations to indicate the investment return, for instance 
CognIA was deployed on IBM Bluemix, a platform as a service, on a Liberty for Java Cloud Foundry app with 3 GB RAM memory and 1 GB disk  Each of the modules shown in Figure FIGREF74 are deployed on separate Bluemix servers  Node JS and Socket IO servers are both deployed as Node Cloud Foundry apps, with 256 MB RAM memory and 512 MB disk each  Search Finance on Social Media is on a Go build pack Cloud Foundry app with 128 MB RAM memory and 128 GB disk  For the framework part that simulates clients, we instantiated a virtual machine with 8 cores on IBM's SoftLayer that is able to communicate with Bluemix  Then, the system testers built two dialogues, i e , INLINEFORM0   The example shown in Table UID98 is the dialogue test INLINEFORM1   For the dialogue INLINEFORM2 , although it also has 10 utterances, the testers varied some of them to check if other utterances in the finance domain (different from the ones in dialogue INLINEFORM3 ) are being responded as expected by the bots  Then, two tests are performed and the results are analyzed next  All tests were repeated until the standard deviation of the values was less than 1%  The results presented next are the average of these values within the 1% margin 
Test 1: The first test consists of running both dialogues INLINEFORM0 and INLINEFORM1 for only one user for sanity check  We set 30 seconds as the maximum time a simulated user should wait for a bot correct response before raising an error  The result is that all chatbots (Mediator, CDBExpert, and SavingsAccountExpert) responded all expected responses before the maximum time  Additionally, the framework collected how long each chatbot took to respond an expected answer 
In Figure FIGREF101 , we show the results for those time measurements for dialogue INLINEFORM0 , as for the dialogue INLINEFORM1 the results are approximately the same  The x-axis (Response Identifier) corresponds to the second column (Resp  Id) in Table UID98   We can see, for example, that when the bot CDBExpert responds with the message 3 to the user utterance "what is cdb?", it is the only bot that takes time different than zero to answer, which is the expected behavior  We can also see that the Mediator bot is the one that takes the longest, as it is responsible to coordinate the other bots and the entire dialogue with the user  Moreover, when the expert bots (CDBExpert and SavingsAccountExpert) are called by the Mediator to respond to the simulation calculations (this happens in responses 6, 7, 11, 12, 16, 17, 21, 22, 26, 27), they take approximately the same to respond  Finally, we see that when the concluding responses to the simulation calculations are given by the Mediator (this happens in responses 9, 14, 19, 24, 29), the response times reaches the greatest values, being 20 seconds the greatest value in response 19  These results support the system developers to understand the behavior of the system when simulated users interact with it and then focus on specific messages that are taking longer 
Test 2: This test consists of running dialogue INLINEFORM0 , but now using eight concurrent simulated users  We set the maximum time to wait to 240 seconds, i e , eight times the maximum set up for the single user in Test 1  The results are illustrated in Figure FIGREF102 , where we show the median time for the eight users  The maximum and minimum values are also presented with horizontal markers  Note that differently than what has been shown in Figure FIGREF101 , where each series represents one specific chatbot, in Figure FIGREF102 , the series represents the median response time for the responses in the order (x-axis) they are responded, regardless the chatbot 
Comparing the results in Figure FIGREF102 with the ones in Figure FIGREF101 , we can see that the bots take longer to respond when eight users are concurrently using the platform than when a single user uses it, as expected  For example, CDBExpert takes approximately 5 times longer to respond response 3 to eight users than to respond to one user  On average, the concluding responses to the simulation questions (i e , responses 9, 14, 19, 24, 29) take approximately 7 3 times more to be responded with eight users than with one user, being the response 9 the one that presented greatest difference (11 4 times longer with eight users than with one)  These results help the system developers to diagnose the scalability of the system architecture and to plan sizing and improvements 
Conclusions and Future Work
In this article, we explored the challenges of engineering MPCS and we have presented a hybrid conceptual architecture and its implementation with a finance advisory system 
We are currently evolving this architecture to be able to support decoupled interaction norms specification, and we are also developing a multi-party governance service that uses that specification to enforce exchange of compliant utterances 
In addition, we are exploring a micro-service implementation of SABIA in order to increase its scalability and performance, so thousands of members can join the system within thousands of conversations 
Acknowledgments
The authors would like to thank Maximilien de Bayser, Ana Paula Appel, Flavio Figueiredo and Marisa Vasconcellos, who contributed with discussions during SABIA and CognIA's implementation 

ground_truth: Custom dataset with user questions; set of documents, twitter posts and news articles, all related to finance.

a self-collected financial intents dataset in Portuguese