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 kind of stylistic features are obtained?
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 

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. Why masking words in the decoder is helpful?
Context 2: Introduction
Text summarization generates summaries from input documents while keeping salient information  It is an important task and can be applied to several real-world applications  Many methods have been proposed to solve the text summarization problem BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3   There are two main text summarization techniques: extractive and abstractive  Extractive summarization generates summary by selecting salient sentences or phrases from the source text, while abstractive methods paraphrase and restructure sentences to compose the summary  We focus on abstractive summarization in this work as it is more flexible and thus can generate more diverse summaries 
Recently, many abstractive approaches are introduced based on neural sequence-to-sequence framework BIBREF4 , BIBREF0 , BIBREF3 , BIBREF5   Based on the sequence-to-sequence model with copy mechanism BIBREF6 , BIBREF0 incorporates a coverage vector to track and control attention scores on source text  BIBREF4 introduce intra-temporal attention processes in the encoder and decoder to address the repetition and incoherent problem 
There are two issues in previous abstractive methods: 1) these methods use left-context-only decoder, thus do not have complete context when predicting each word  2) they do not utilize the pre-trained contextualized language models on the decoder side, so it is more difficult for the decoder to learn summary representations, context interactions and language modeling together 
Recently, BERT has been successfully used in various natural language processing tasks, such as textual entailment, name entity recognition and machine reading comprehensions  In this paper, we present a novel natural language generation model based on pre-trained language models (we use BERT in this work)  As far as we know, this is the first work to extend BERT to the sequence generation task  To address the above issues of previous abstractive methods, in our model, we design a two-stage decoding process to make good use of BERT's context modeling ability  On the first stage, we generate the summary using a left-context-only-decoder  On the second stage, we mask each word of the summary and predict the refined word one-by-one using a refine decoder  To further improve the naturalness of the generated sequence, we cooperate reinforcement objective with the refine decoder 
The main contributions of this work are:
1  We propose a natural language generation model based on BERT, making good use of the pre-trained language model in the encoder and decoder process, and the model can be trained end-to-end without handcrafted features 
2  We design a two-stage decoder process  In this architecture, our model can generate each word of the summary considering both sides' context information 
3  We conduct experiments on the benchmark datasets CNN/Daily Mail and New York Times  Our model achieves a 33 33 average of ROUGE-1, ROUGE-2 and ROUGE-L on the CNN/Daily Mail, which is state-of-the-art  On the New York Times dataset, our model achieves about 5 6% relative improvement over ROUGE-1 
Text Summarization
In this paper, we focus on single-document multi-sentence summarization and propose a supervised abstractive model based on the neural attentive sequence-to-sequence framework which consists of two parts: a neural network for the encoder and another network for the decoder  The encoder encodes the input sequence to intermediate representation and the decoder predicts one word at a time step given the input sequence representation vector and previous decoded output  The goal of the model is to maximize the probability of generating the correct target sequences  In the encoding and generation process, the attention mechanism is used to concentrate on the most important positions of text  The learning objective of most sequence-to-sequence models is to minimize the negative log likelihood of the generated sequence as following equation shows, where $y^*_i$ is the i-th ground-truth summary token 
$$Loss = - \log \sum _{t=1}^N P(y_t^*|y_{<t}^*, X)$$   (Eq  3)
However, with this objective, traditional sequence generation models consider only one direction context in the decoding process, which could cause performance degradation since complete context of one token contains preceding and following tokens, thus feeding only preceded decoded words to the decoder so that the model may generate unnatural sequences  For example, attentive sequence-to-sequence models often generate sequences with repeated phrases which harm the naturalness  Some previous works mitigate this problem by improving the attention calculation process, but in this paper we show that feeding bi-directional context instead of left-only-context can better alleviate this problem 
Text summarization models are usually classified to abstractive and extractive ones  Recently, extractive models like DeepChannel BIBREF8 , rnn-ext+RL BIBREF9 and NeuSUM BIBREF2 achieve higher performances using well-designed structures  For example, DeepChannel propose a salience estimation network and iteratively extract salient sentences  BIBREF16 train a sentence compression model to teach another latent variable extractive model 
Also, several recent works focus on improving abstractive methods  BIBREF3 design a content selector to over-determine phrases in a source document that should be part of the summary  BIBREF11 introduce inconsistency loss to force words in less attended sentences(which determined by extractive model) to have lower generation probabilities  BIBREF5 extend seq2seq model with an information selection network to generate more informative summaries 
Bi-Directional Pre-Trained Context Encoders
Recently, context encoders such as ELMo, GPT, and BERT have been widely used in many NLP tasks  These models are pre-trained on a huge unlabeled corpus and can generate better contextualized token embeddings, thus the approaches built on top of them can achieve better performance 
Since our method is based on BERT, we illustrate the process briefly here  BERT consists of several layers  In each layer there is first a multi-head self-attention sub-layer and then a linear affine sub-layer with the residual connection  In each self-attention sub-layer the attention scores $e_{ij}$ are first calculated as Eq  ( 5 ) () shows, in which $d_e$ is output dimension, and $W^Q, W^K, W^V$ are parameter matrices 
$$&a_{ij} = \cfrac{(h_iW_Q)(h_jW_K)^T}{\sqrt{d_e}}  \\ &e_{ij} = \cfrac{\exp {e_{ij}}}{\sum _{k=1}^N\exp {e_{ik}}} $$   (Eq  5)
Then the output is calculated as Eq  ( 6 ) shows, which is the weighted sum of previous outputs $h$ added by previous output $h_i$   The last layer outputs is context encoding of input sequence 
$$o_i = h_i + \sum _{j=1}^{N} e_{ij}(h_j W_V) $$   (Eq  6)
Despite the wide usage and huge success, there is also a mismatch problem between these pre-trained context encoders and sequence-to-sequence models  The issue is that while using a pre-trained context encoder like GPT or BERT, they model token-level representations by conditioning on both direction context  During pre-training, they are fed with complete sequences  However, with a left-context-only decoder, these pre-trained language models will suffer from incomplete and inconsistent context and thus cannot generate good enough context-aware word representations, especially during the inference process 
Model
In this section, we describe the structure of our model, which learns to generate an abstractive multi-sentence summary from a given source document 
Based on the sequence-to-sequence framework built on top of BERT, we first design a refine decoder at word-level to tackle the two problems described in the above section  We also introduce a discrete objective for the refine decoders to reduce the exposure bias problem  The overall structure of our model is illustrated in Figure 1  
Problem Formulation
We denote the input document as $X = \lbrace x_1, \ldots , x_m\rbrace $ where $x_i \in \mathcal {X}$ represents one source token  The corresponding summary is denoted as $Y = \lbrace y_1, \ldots , y_L\rbrace $ , $L$ represents the summary length 
Given input document $X$ , we first predict the summary draft by a left-context-only decoder, and then using the generated summary draft we can condition on both context sides and refine the content of the summary  The draft will guide and constrain the refine process of summary 
Summary Draft Generation
The summary draft is based on the sequence-to-sequence model  On the encoder side the input document $X$ is encoded into representation vectors $H = \lbrace h_1, \ldots , h_m\rbrace $ , and then fed to the decoder to generate the summary draft $A = \lbrace a_1, \ldots , a_{|a|}\rbrace $  
We simply use BERT as the encoder  It first maps the input sequence to word embeddings and then computes document embeddings as the encoder's output, denoted by following equation 
$$H = BERT(x_1, \ldots , x_m)$$   (Eq  10)
In the draft decoder, we first introduce BERT's word embedding matrix to map the previous summary draft outputs $\lbrace y_1, \ldots , y_{t-1}\rbrace $ into embeddings vectors $\lbrace q_1, \ldots , q_{t-1}\rbrace $ at t-th time step  Note that as the input sequence of the decoder is not complete, we do not use the BERT network to predict the context vectors here 
Then we introduce an $N$ layer Transformer decoder to learn the conditional probability $P(A|H)$   Transformer's decoder-encoder multi-head attention helps the decoder learn soft alignments between summary and source document  At the t-th time step, the draft decoder predicts output probability conditioned on previous outputs and encoder hidden representations as Eq  ( 13 ) shows, in which $q_{<t} = \lbrace q_1, \ldots , q_{t-1}\rbrace $   Each generated sequence will be truncated in the first position of a special token '[PAD]' 
$$&P^{vocab}_t(w) = f_{dec}(q_{<t}, H)  \\ &L_{dec} = \sum _{i=1}^{|a|} -\log P(a_i = y_i^*|a_{< i}, H) $$   (Eq  13)
As Eq  () shows, the decoder's learning objective is to minimize negative likelihood of conditional probability, in which $y_i^*$ is the i-th ground truth word of summary 
However a decoder with this structure is not sufficient enough: if we use the BERT network in this decoder, then during training and inference, in-complete context(part of sentence) is fed into the BERT module, and although we can fine-tune BERT's parameters, the input distribution is quite different from the pre-train process, and thus harms the quality of generated context representations 
If we just use the embedding matrix here, it will be more difficult for the decoder with fresh parameters to learn to model representations as well as vocabulary probabilities, from a relative small corpus compared to BERT's huge pre-training corpus  In a word, the decoder cannot utilize BERT's ability to generate high quality context vectors, which will also harm performance 
This issue exists when using any other contextualized word representations, so we design a refine process to mitigate it in our approach which will be described in the next sub-section 
As some summary tokens are out-of-vocabulary words and occurs in input document, we incorporate copy mechanism BIBREF6 based on the Transformer decoder, we will describe it briefly 
At decoder time step $t$ , we first calculate the attention probability distribution over source document $X$ using the bi-linear dot product of the last layer decoder output of Transformer $o_t$ and the encoder output $h_j$ , as Eq  ( 15 ) () shows 
$$u_t^j =& o_t W_c h_j \\ \alpha _t^j =& \cfrac{\exp {u_t^j}}{\sum _{k=1}^N\exp {u_t^k}} $$   (Eq  15)
We then calculate copying gate $g_t\in [0, 1]$ , which makes a soft choice between selecting from source and generating from vocabulary, $W_c, W_g, b_g$ are parameters:
$$g_t = sigmoid(W_g \cdot [o_t, h] + b_g) $$   (Eq  16)
Using $g_t$ we calculate the weighted sum of copy probability and generation probability to get the final predicted probability of extended vocabulary $\mathcal {V} + \mathcal {X}$ , where $\mathcal {X}$ is the set of out of vocabulary words from the source document  The final probability is calculated as follow:
$$P_t(w) = (1-g_t)P_t^{vocab}(w) + g_t\sum _{i:w_i=w} \alpha _t^i$$   (Eq  17)
Summary Refine Process
The main reason to introduce the refine process is to enhance the decoder using BERT's contextualized representations, so we do not modify the encoder and reuse it during this process 
On the decoder side, we propose a new word-level refine decoder  The refine decoder receives a generated summary draft as input, and outputs a refined summary  It first masks each word in the summary draft one by one, then feeds the draft to BERT to generate context vectors  Finally it predicts a refined summary word using an $N$ layer Transformer decoder which is the same as the draft decoder  At t-th time step the n-th word of input summary is masked, and the decoder predicts the n-th refined word given other words of the summary 
The learning objective of this process is shown in Eq  ( 19 ), $y_i$ is the i-th summary word and $y_{i}^*$ for the ground-truth summary word, and $a_{\ne i} = \lbrace a_1, \ldots , a_{i-1}, a_{i+1}, \ldots , a_{|y|}\rbrace $  
$$L_{refine} = \sum _{i=1}^{|y|} -\log P(y_i = y_i^*|a_{\ne i}, H) $$   (Eq  19)
From the view of BERT or other contextualized embeddings, the refine decoding process provides a more complete input sequence which is consistent with their pre-training processes  Intuitively, this process works as follows: first the draft decoder writes a summary draft based on a document, and then the refine decoder edits the draft  It concentrates on one word at a time, based on the source document as well as other words 
We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences 
The parameters are shared between the draft decoder and refine decoder, as we find that using individual parameters the model's performance degrades a lot  The reason may be that we use teach-forcing during training, and thus the word-level refine decoder learns to predict words given all the other ground-truth words of summary  This objective is similar to the language model's pre-train objective, and is probably not enough for the decoder to learn to generate refined summaries  So in our model all decoders share the same parameters 
Researchers usually use ROUGE as the evaluation metric for summarization, however during sequence-to-sequence model training, the objective is to maximize the log likelihood of generated sequences  This mis-match harms the model's performance, so we add a discrete objective to the model, and optimize it by introducing the policy gradient method  For example, the discrete objective for the summary draft process is as Eq  ( 21 ) shows, where $a^s$ is the draft summary sampled from predicted distribution, and $R(a^s)$ is the reward score compared with the ground-truth summary, we use ROUGE-L in our experiment  To balance between optimizing the discrete objective and generating readable sequences, we mix the discrete objective with maximum-likelihood objective  As Eq  () shows, minimizing $\hat{L}_{dec}$ is the final objective for the draft process, note here $L_{dec}$ is $-logP(a|x)$   In the refine process we introduce similar objectives 
$$L^{rl}_{dec} = R(a^s)\cdot [-\log (P(a^s|x))]  \\ \hat{L}_{dec} = \gamma * L^{rl}_{dec} + (1 - \gamma ) * L_{dec} $$   (Eq  21)
Learning and Inference
During model training, the objective of our model is sum of the two processes, jointly trained using "teacher-forcing" algorithm  During training we feed the ground-truth summary to each decoder and minimize the objective 
$$L_{model} = \hat{L}_{dec} + \hat{L}_{refine}$$   (Eq  23)
At test time, each time step we choose the predicted word by $\hat{y} = argmax_{y^{\prime }} P(y^{\prime }|x)$ , use beam search to generate the draft summaries, and use greedy search to generate the refined summaries 
Settings
In this work, all of our models are built on $BERT_{BASE}$ , although another larger pre-trained model with better performance ( $BERT_{LARGE}$ ) has published but it costs too much time and GPU memory  We use WordPiece embeddings with a 30,000 vocabulary which is the same as BERT  We set the layer of transformer decoders to 12(8 on NYT50), and set the attention heads number to 12(8 on NYT50), set fully-connected sub-layer hidden size to 3072  We train the model using an Adam optimizer with learning rate of $3e-4$ , $\beta _1=0 9$ , $\beta _2=0 999$ and $\epsilon =10^{-9}$ and use a dynamic learning rate during the training process  For regularization, we use dropout BIBREF13 and label smoothing BIBREF14 in our models and set the dropout rate to 0 15, and the label smoothing value to 0 1  We set the RL objective factor $\gamma $ to 0 99 
During training, we set the batch size to 36, and train for 4 epochs(8 epochs for NYT50 since it has many fewer training samples), after training the best model are selected from last 10 models based on development set performance  Due to GPU memory limit, we use gradient accumulation, set accumulate step to 12 and feed 3 samples at each step  We use beam size 4 and length penalty of 1 0 to generate logical form sequences 
We filter repeated tri-grams in beam-search process by setting word probability to zero if it will generate an tri-gram which exists in the existing summary  It is a nice method to avoid phrase repetition since the two datasets seldom contains repeated tri-grams in one summary  We also fine tune the generated sequences using another two simple rules  When there are multi summary sentences with exactly the same content, we keep the first one and remove the other sentences; we also remove sentences with less than 3 words from the result 
To evaluate the performance of our model, we conduct experiments on CNN/Daily Mail dataset, which is a large collection of news articles and modified for summarization  Following BIBREF0 we choose the non-anonymized version of the dataset, which consists of more than 280,000 training samples and 11490 test set samples 
We also conduct experiments on the New York Times(NYT) dataset which also consists of many news articles  The original dataset can be applied here  In our experiment, we follow the dataset splits and other pre-process settings of BIBREF15   We first filter all samples without a full article text or abstract and then remove all samples with summaries shorter than 50 words  Then we choose the test set based on the date of publication(all examples published after January 1, 2007)  The final dataset contains 22,000 training samples and 3,452 test samples and is called NYT50 since all summaries are longer than 50 words 
We tokenize all sequences of the two datasets using the WordPiece tokenizer  After tokenizing, the average article length and summary length of CNN/Daily Mail are 691 and 51, and NYT50's average article length and summary length are 1152 and 75  We truncate the article length to 512, and the summary length to 100 in our experiment(max summary length is set to 150 on NYT50 as its average golden summary length is longer) 
On CNN/Daily Mail dataset, we report the full-length F-1 score of the ROUGE-1, ROUGE-2 and ROUGE-L metrics, calculated using PyRouge package and the Porter stemmer option  On NYT50, following BIBREF4 we evaluate limited length ROUGE recall score(limit the generated summary length to the ground truth length)  We split NYT50 summaries into sentences by semicolons to calculate the ROUGE scores 
Results and Analysis
Table 1 shows the results on CNN/Daily Mail dataset, we compare the performance of many recent approaches with our model  We classify them to two groups based on whether they are extractive or abstractive models  As the last line of the table shows, the ROUGE-1 and ROUGE-2 score of our full model is comparable with DCA, and outperforms on ROUGE-L  Also, compared to extractive models NeuSUM and MASK- $LM^{global}$ , we achieve slight higher ROUGE-1  Except the four scores, our model outperforms these models on all the other scores, and since we have 95% confidence interval of at most $\pm $ 0 20, these improvements are statistically significant 
As the last four lines of Table 1 show, we conduct an ablation study on our model variants to analyze the importance of each component  We use three ablation models for the experiments  One-Stage: A sequence-to-sequence model with copy mechanism based on BERT; Two-Stage: Adding the word-refine decoder to the One-Stage model; Two-Stage + RL: Full model with refine process cooperated with RL objective 
First, we compare the Two-Stage+RL model with Two-Stage ablation, we observe that the full model outperforms by 0 30 on average ROUGE, suggesting that the reinforcement objective helps the model effectively  Then we analyze the effect of refine process by removing word-level refine from the Two-Stage model, we observe that without the refine process the average ROUGE score drops by 1 69  The ablation study shows that each module is necessary for our full model, and the improvements are statistically significant on all metrics 
To evaluate the impact of summary length on model performance, we compare the average rouge score improvements of our model with different length of ground-truth summaries  As the above sub-figure of Figure 2 shows, compared to Pointer-Generator with Coverage, on length interval 40-80(occupies about 70% of test set) the improvements of our model are higher than shorter samples, confirms that with better context representations, in longer documents our model can achieve higher performance 
As the below sub-figure of Figure 2 shows, compared to extractive baseline: Lead-3 BIBREF0 , the advantage of our model will fall when golden summary length is greater than 80  This probably because that we truncate the long documents and golden summaries and cannot get full information, it could also because that the training data in these intervals is too few to train an abstractive model, so simple extractive method will not fall too far behind 
Additional Results on NYT50
Table 2 shows experiments on the NYT50 corpus  Since the short summary samples are filtered, NYT50 has average longer summaries than CNN/Daily Mail  So the model needs to catch long-term dependency of the sequences to generate good summaries 
The first two lines of Table 2 show results of the two baselines introduced by BIBREF15 : these baselines select first n sentences, or select the first k words from the original document  Also we compare performance of our model with two recent models, we see 2 39 ROUGE-1 improvements compared to the ML+RL with intra-attn approach(previous SOTA) carries over to this dataset, which is a large margin  On ROUGE-2, our model also get an improvement of 0 51  The experiment proves that our approach can outperform competitive methods on different data distributions 
Pre-trained language models
Pre-trained word vectors BIBREF17 , BIBREF18 , BIBREF19 have been widely used in many NLP tasks  More recently, pre-trained language models (ELMo, GPT and BERT), have also achieved great success on several NLP problems such as textual entailment, semantic similarity, reading comprehension, and question answering BIBREF20 , BIBREF21 , BIBREF22  
Some recent works also focus on leveraging pre-trained language models in summarization  BIBREF23 pretrain a language model and use it as the sentiment analyser when generating reviews of goods  BIBREF24 train a language model on golden summaries, and then use it on the decoder side to incorporate prior knowledge 
In this work, we use BERT(which is a pre-trained language model using large scale unlabeled data) on the encoder and decoder of a seq2seq model, and by designing a two stage decoding structure we build a competitive model for abstractive text summarization 
Conclusion and Future Work
In this work, we propose a two-stage model based on sequence-to-sequence paradigm  Our model utilize BERT on both encoder and decoder sides, and introduce reinforce objective in learning process  We evaluate our model on two benchmark datasets CNN/Daily Mail and New York Times, the experimental results show that compared to previous systems our approach effectively improves performance 
Although our experiments are conducted on summarization task, our model can be used in most natural language generation tasks, such as machine translation, question generation and paraphrasing  The refine decoder and mixed objective can also be applied on other sequence generation tasks, and we will investigate on them in future work 
Context 3: Introduction
Part-of-speech tagging is now a classic task in natural language processing, for which many systems have been developed or adapted for a large variety of languages  Its aim is to associate each “word” with a morphosyntactic tag, whose granularity can range from a simple morphosyntactic category, or part-of-speech (hereafter PoS), to finer categories enriched with morphological features (gender, number, case, tense, mood, etc ) 
The use of machine learning algorithms trained on manually annotated corpora has long become the standard way to develop PoS taggers  A large variety of algorithms have been used, such as (in approximative chronological order) bigram and trigram hidden Markov models BIBREF0 , BIBREF1 , BIBREF2 , decision trees BIBREF3 , BIBREF4 , maximum entropy Markov models (MEMMs) BIBREF5 and Conditional Random Fields (CRFs) BIBREF6 , BIBREF7   With such machine learning algorithms, it is possible to build PoS taggers for any language, provided adequate training data is available 
As a complement to annotated corpora, it has previously been shown that external lexicons are valuable sources of information, in particular morphosyntactic lexicons, which provide a large inventory of (word, PoS) pairs  Such lexical information can be used in the form of constraints at tagging time BIBREF8 , BIBREF9 or during the training process as additional features combined with standard features extracted from the training corpus BIBREF10 , BIBREF11 , BIBREF12  
In recent years, a different approach to modelling lexical information and integrating it into natural language processing systems has emerged, namely the use of vector representations for words or word sequences BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18   Such representations, which are generally extracted from large amounts of raw text, have proved very useful for numerous tasks including PoS tagging, in particular when used in recurrent neural networks (RNNs) and more specifically in mono- or bi-directional, word-level and/or character-level long short-term memory networks (LSTMs) BIBREF19 , BIBREF16 , BIBREF17 , BIBREF20  
Both approaches to representing lexical properties and to integrating them into a PoS tagger improve tagging results  Yet they rely on resources of different natures  The main advantage of word vectors is that they are built in an unsupervised way, only requiring large amounts of raw textual data  They also encode finer-grained information than usual morphosyntactic lexicons, most of which do not include any quantitative data, not even simple frequency information  Conversely, lexical resources often provide information about scarcely attested words, for which corpus-based approaches such as word vector representations are of limited relevance  Moreover, morphological or morphosyntactic lexicons already exist for a number of languages, including less-resourced langauges for which it might be difficult to obtain the large amounts of raw data necessary to extract word vector representations 
Our main goal is therefore to compare the respective impact of external lexicons and word vector representations on the accuracy of PoS models  This question has already been investigated for 6 languages by BIBREF18 using the state-of-the-art CRF-based tagging system MarMoT  The authors found that their best-performing word-vector-based PoS tagging models outperform their models that rely on morphosyntactic resources (lexicons or morphological analysers)  In this paper, we report on larger comparison, carried out in a larger multilingual setting and comparing different tagging models  Using different 16 datasets, we compare the performances of two feature-based models enriched with external lexicons and of two LSTM-based models enriched with word vector representations  A secondary goal of our work is to compare the relative improvements linked to the use of external lexical information in the two feature-based models, which use different models (MEMM vs  CRF) and feature sets 
More specifically, our starting point is the MElt system BIBREF12 , an MEMM tagging system  We first briefly describe this system and the way we adapted it by integrating our own set of corpus-based and lexical features  We then introduce the tagging models we have trained for 16 different languages using our adapted version of MElt  These models are trained on the Universal Dependencies (v1 2) corpus set BIBREF21 , complemented by morphosyntactic lexicons  We compare the accuracy of our models with the scores obtained by the CRF-based system MarMoT BIBREF22 , BIBREF18 , retrained on the same corpora and the same external morphosyntactic lexicons  We also compare our results to those obtained by the best bidirectional LSTM models described by BIBREF20 , which both make use of Polyglot word vector representations published by BIBREF23   We will show that an optimised enrichment of feature-based models with morphosyntactic lexicon results in significant accuracy gains  The macro-averaged accuracy of our enriched MElt models is above that of enriched MarMoT models and virtually identical to that of LSTMs enriched with word vector representations  More precisely, per-language results indicate that lexicons provide more useful information for languages with a high lexical variability (such as morphologically rich languages), whereas word vectors are more informative for languages with a lower lexical variability (such as English) 
MElt
MElt BIBREF12 is a tagging system based on maximum entropy Markov models (MEMM) BIBREF5 , a class of discriminative models that are suitable for sequence labelling BIBREF5   The basic set of features used by MElt is given in BIBREF12   It is a superset of the feature sets used by BIBREF5 and BIBREF24 and includes both local standard features (for example the current word itself and its prefixes and suffixes of length 1 to 4) and contextual standard features (for example the tag just assigned to the preceding word)  In particular, with respect to Ratnaparkhi's feature set, MElt's basic feature set lifts the restriction that local standard features used to analyse the internal composition of the current word should only apply to rare words 
One of the advantages of feature-based models such as MEMMs and CRFs is that complementary information can be easily added in the form of additional features  This was investigated for instance by BIBREF25 , whose best-performing model for PoS tagging dialogues was obtained with a version of MElt extended with dialogue-specific features  Yet the motivation of MElt's developers was first and foremost to investigate the best way to integrate lexical information extracted from large-scale morphosyntactic lexical resources into their models, on top of the training data BIBREF12   They showed that performances are better when this external lexical information is integrated in the form of additional lexical features than when the external lexicon is used as constraints at tagging time  These lexical features can also be divided into local lexical features (for example the list of possible tags known to the external lexicon for the current word) and contextual lexical features (for example the list of possible tags known to the external lexicon for surrounding words)  In particular, lexical contextual features provide a means to model the right context of the current word, made of words that have not yet been tagged by the system but for which the lexicon often provides a list of possible tags  Moreover, tagging accuracy for out-of-vocabulary (OOV) words is improved, as a result of the fact that words unknown to the training corpus might be known to the external lexicon 
Despite a few experiments published with MElt on languages other than French BIBREF12 , BIBREF40 , BIBREF41 , the original feature set used by MElt (standard and lexical features) was designed and tested mostly on this language, by building and evaluating tagging models on a variant of the French TreeBank  Since our goal was to carry out experiments in a multilingual setting, we have decided to design our own set of features, using the standard MElt features as a starting point  With respect to the original MElt feature set, we have added new ones, such as prefixes and suffixes of the following word, as well as a hybrid contextual feature obtained by concatenating the tag predicted for the preceding word and the tag(s) provided by the external lexicon for the following word 
In order to select the best performing feature set, we carried out a series of experiments using the multilingual dataset provided during the SPMRL parsing shared task BIBREF42   This included discarding useless or harmful features and selecting the maximal length of the prefixes and suffixes to be used as features, both for the current word and for the following word 
We incorporated in MElt the best performing feature set, described in Table TABREF1   All models discussed in this paper are based on this feature set 
Corpora
We carried out our experiments on the Universal Dependencies v1 2 treebanks BIBREF21 , hereafter UD1 2, from which morphosyntactically annotated corpora can be trivially extracted  All UD1 2 corpora use a common tag set, the 17 universal PoS tags, which is an extension of the tagset proposed by BIBREF43  
As our goal is to study the impact of lexical information for PoS tagging, we have restricted our experiments to UD1 2 corpora that cover languages for which we have morphosyntactic lexicons at our disposal, and for which BIBREF20 provide results  We considered UD1 2 corpora for the following 16 languages: Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish and Swedish  Although this language list contains only one non-Indo-European (Indonesian), four major Indo-European sub-families are represented (Germanic, Romance, Slavic, Indo-Iranian)  Overall, the 16 languages considered in our experiments are typologically, morphologically and syntactically fairly diverse 
Lexicons
We generate our external lexicons using the set of source lexicons listed in Table TABREF3   Since external lexical information is exploited via features, there is no need for the external lexicons and the annotated corpora to use the same PoS inventory  Therefore, for each language, we simply extracted from the corresponding lexicon the PoS of each word based on its morphological tags, by removing all information provided except for its coarsest-level category  We also added entries for punctuations when the source lexicons did not contain any 
We also performed experiments in which we retained the full original tags provided by the lexicons, with all morphological features included  On average, results were slightly better than those presented in the paper, although not statistically significantly  Moreover, the granularity of tag inventories in the lexicons is diverse, which makes it difficult to draw general conclusions about results based on full tags  This is why we only report results based on (coarse) PoS extracted from the original lexicons 
Baseline models
In order to assess the respective contributions of external lexicons and word vector representations, we first compared the results of the three above-mentioned systems when trained without such additional lexical information  Table TABREF11 provides the results of MElt and MarMoT retrained on UD1 2 corpora, together with the results publised on the same corpora by BIBREF20 , using their best model not enhanced by external word vector representations —i e  the model they call INLINEFORM0 , which is a bidirectional LSTM that combines both word and character embeddings 
These results show that Plank et al 's (2016) bi-LSTM performs extremely well, surpassed by MarMoT on only 3 out of 16 datasets (Czech, French and Italian), and by MElt only once (Indonesian) 
Models enriched with external lexical information
Table TABREF13 provides the results of four systems enriched with lexical information  The feature-based systems MElt and MarMoT, respectively based on MEMMs and CRFs, are extended with the lexical information provided by our morphosyntactic lexicons  This extension takes the form of additional features, as described in Section SECREF2 for MElt  The results reported by BIBREF20 for their bidirectional LSTM when initialised with Polyglot embeddings trained on full wikipedias are also included, together with their new system FREQBIN, also initialised with Polyglot embeddings  FREQBIN trains bi-LSTMs to predict for each input word both a PoS and a label that represents its log frequency in the training data  As they word it, “the idea behind this model is to make the representation predictive for frequency, which encourages the model not to share representations between common and rare words, thus benefiting the handling of rare tokens ”
The results, which are also displayed in Figures FIGREF14 and FIGREF15 , show that all systems reach very similar results on average, although discrepancies can be observed from one dataset to another, on which we shall comment shortly  The best performing system in terms of macro-average is MElt (96 60%)  Both bi-LSTM systems reach the same score (96 58%), the difference with MElt's results being non significant, whereas MarMoT is only 0 14% behind (96 46%)  Given the better baseline scores of the neural approaches, these results show that the benefit of using external lexicons in the feature-based models MElt and MarMoT are much higher than those using Polyglot word vector representations as initialisations for bi-LSTMs 
Yet these very similar overall results reflect a different picture when focusing on OOV tagging accuracy  The best models for OOV tagging accuracy are, by far, FREQBIN models, which are beaten by MarMoT and by MElt only once each (on English and Danish respectively)  The comparison on OOV tagging between MElt and MarMoT shows that MElt performs better on average than MarMoT, despite the fact that MarMoT's baseline results were better than those reached by MElt  This shows that the information provided by external morphosyntactic lexicons is better exploited by MElt's lexical features than by those used by MarMoT  On the other hand, the comparison of both bi-LSTM-based approaches confirm that the FREQBIN models is better by over 10% absolute on OOV tagging accuracy (94 28% vs  83 59%), with 65% lower error rate 
One of the important differences between the lexical information provided by an external lexicon and word vectors built from raw corpora, apart from the very nature of the lexical information provided, is the coverage and accuracy of this lexical information on rare words  All words in a morphosyntactic lexicon are associated with information of a same granularity and quality, which is not the case with word representations such as provided by Polyglot  Models that take advantage of external lexicons should therefore perform comparatively better on datasets containing a higher proportion of rarer words, provided the lexicons' coverage is high  In order to confirm this intuition, we have used a lexical richness metric based on the type/token ratio  Since this ratio is well-known for being sensitive to corpus length, we normalised it by computing it over the 60,000 first tokens of each training set  When this normalised type/token ratio is plotted against the difference between the results of MElt and both bi-LSTM-based models, the expected correlation is clearly visible (see Figure FIGREF16 )  This explains why MElt obtains better results on the morphologically richer Slavic datasets (average normalised type/token ratio: 0 28, average accuracy difference: 0 32 compared to both bi-LSTM+Polyglot and FREQBIN+Polyglot) and, at the other end of the spectrum, significantly worse results on the English dataset (normalised type/token ratio: 0 15, average accuracy difference: -0 56 compared to bi-LSTM+Polyglot, -0 57 compared to FREQBIN+Polyglot) 
Conclusion
Two main conclusions can be drawn from our comparative results  First, feature-based tagging models adequately enriched with external morphosyntactic lexicons perform, on average, as well as bi-LSTMs enriched with word embeddings  Per-language results show that the best accuracy levels are reached by feature-based models, and in particular by our improved version of the MEMM-based system MElt, on datasets with high lexical variability (in short, for morphologically rich languages), whereas neural-based results perform better on datatsets with lower lexical variability (e g  for English) 
We have only compared the contribution of morphosyntactic lexicons to feature-based models (MEMMs, CRFs) and that of word vector representations to bi-LSTM-based models as reported by BIBREF20   As mentioned above, work on the contribution of word vector representations to feature-based approaches has been carried out by BIBREF18   However, the exploitation of existing morphosyntactic or morphological lexicons in neural models is a less studied question  Improvements over the state of the art might be achieved by integrating lexical information both from an external lexicon and from word vector representations into tagging models 
In that regard, further work will be required to understand which class of models perform the best  An option would be to integrate feature-based models such as a CRF with an LSTM-based layer, following recent proposals such as the one proposed by BIBREF45 for named entity recognition 

ground_truth: ecause this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.

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. which languages are explored?
Context 4:  Equation (DISPLAY_FORM13) can ensure the Gaussian weight equals 1 when $dis_{ij}$ is 0  The larger distance between charactersis, the smaller the weight is, which makes one character can affect its adjacent characters more compared with other characters 
To combine the Gaussian weight to the self-attention, we produce the Hadamard product of Gaussian weight matrix $G$ and the score matrix produced by $Q{K^{T}}$
where $AG$ is the Gaussian-masked attention  It ensures that the relationship between two characters with long distances is weaker than adjacent characters 
The scaled dot-product attention models the relationship between two characters without regard to their distances in one sequence  For CWS task, the weight between adjacent characters should be more important while it is hard for self-attention to achieve the effect explicitly because the self-attention cannot get the order of sentences directly  The Gaussian-masked attention adjusts the weight between characters and their adjacent character to a larger value which stands for the effect of adjacent characters 
For forward and backward encoder, the self-attention sublayer needs to use a triangular matrix mask to let the self-attention focus on different weights:
where $pos_i$ is the position of character $c_i$  The triangular matrix for forward and backward encode are:
$\left[ \begin{matrix} 1 & 0 & 0 & \cdots &0\\ 1 & 1 & 0 & \cdots &0\\ 1 & 1 & 1 & \cdots &0\\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 1 & 1 & 1 & \cdots & 1\\ \end{matrix} \right]$ $\left[ \begin{matrix} 1 & 1 & 1 & \cdots &1 \\ 0 & 1 & 1 & \cdots &1 \\ 0 & 0& 1 & \cdots &1 \\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 0 & 0 & 0 & \cdots & 1\\ \end{matrix}\right]$
Similar as BIBREF24, we use multi-head attention to capture information from different dimension positions as Figure FIGREF16 and get Gaussian-masked directional multi-head attention  With multi-head attention architecture, the representation of input can be captured by
where $MH$ is the Gaussian-masked multi-head attention, ${W_i^q, W_i^k,W_i^v} \in \mathbb {R}^{d_k \times d_h}$ is the parameter matrices to generate heads, $d_k$ is the dimension of model and $d_h$ is the dimension of one head 
Models ::: Bi-affinal Attention Scorer
Regarding word boundaries as gaps between any adjacent words converts the character labeling task to the gap labeling task  Different from character labeling task, gap labeling task requires information of two adjacent characters  The relationship between adjacent characters can be represented as the type of gap  The characteristic of word boundaries makes bi-affine attention an appropriate scorer for CWS task 
Bi-affinal attention scorer is the component that we use to label the gap  Bi-affinal attention is developed from bilinear attention which has been used in dependency parsing BIBREF26 and SRL BIBREF27  The distribution of labels in a labeling task is often uneven which makes the output layer often include a fixed bias term for the prior probability of different labels BIBREF27  Bi-affine attention uses bias terms to alleviate the burden of the fixed bias term and get the prior probability which makes it different from bilinear attention  The distribution of the gap is uneven that is similar as other labeling task which fits bi-affine 
Bi-affinal attention scorer labels the target depending on information of independent unit and the joint information of two units  In bi-affinal attention, the score $s_{ij}$ of characters $c_i$ and $c_j$ $(i < j)$ is calculated by:
where $v_i^f$ is the forward information of $c_i$ and $v_i^b$ is the backward information of $c_j$  In Equation (DISPLAY_FORM21), $W$, $U$ and $b$ are all parameters that can be updated in training  $W$ is a matrix with shape $(d_i \times N\times d_j)$ and $U$ is a $(N\times (d_i + d_j))$ matrix where $d_i$ is the dimension of vector $v_i^f$ and $N$ is the number of labels 
In our model, the biaffine scorer uses the forward information of character in front of the gap and the backward information of the character behind the gap to distinguish the position of characters  Figure FIGREF22 is an example of labeling gap  The method of using biaffine scorer ensures that the boundaries of words can be determined by adjacent characters with different directional information  The score vector of the gap is formed by the probability of being a boundary of word  Further, the model generates all boundaries using activation function in a greedy decoding way 
Experiments ::: Experimental Settings ::: Data
We train and evaluate our model on datasets from SIGHAN Bakeoff 2005 BIBREF21 which has four datasets, PKU, MSR, AS and CITYU  Table TABREF23 shows the statistics of train data  We use F-score to evaluate CWS models  To train model with pre-trained embeddings in AS and CITYU, we use OpenCC to transfer data from traditional Chinese to simplified Chinese 
Experiments ::: Experimental Settings ::: Pre-trained Embedding
We only use unigram feature so we only trained character embeddings  Our pre-trained embedding are pre-trained on Chinese Wikipedia corpus by word2vec BIBREF29 toolkit  The corpus used for pre-trained embedding is all transferred to simplified Chinese and not segmented  On closed test, we use embeddings initialized randomly 
Experiments ::: Experimental Settings ::: Hyperparameters
For different datasets, we use two kinds of hyperparameters which are presented in Table TABREF24  We use hyperparameters in Table TABREF24 for small corpora (PKU and CITYU) and normal corpora (MSR and AS)  We set the standard deviation of Gaussian function in Equation (DISPLAY_FORM13) to 2  Each training batch contains sentences with at most 4096 tokens 
Experiments ::: Experimental Settings ::: Optimizer
To train our model, we use the Adam BIBREF30 optimizer with $\beta _1=0 9$, $\beta _2=0 98$ and $\epsilon =10^{-9}$  The learning rate schedule is the same as BIBREF24:
where $d$ is the dimension of embeddings, $step$ is the step number of training and $warmup_step$ is the step number of warmup  When the number of steps is smaller than the step of warmup, the learning rate increases linearly and then decreases 
Experiments ::: Hardware and Implements
We trained our models on a single CPU (Intel i7-5960X) with an nVidia 1080 Ti GPU  We implement our model in Python with Pytorch 1 0 
Experiments ::: Results
Tables TABREF25 and TABREF26 reports the performance of recent models and ours in terms of closed test setting  Without the assistance of unsupervised segmentation features userd in BIBREF20, our model outperforms all the other models in MSR and AS except BIBREF18 and get comparable performance in PKU and CITYU  Note that all the other models for this comparison adopt various $n$-gram features while only our model takes unigram ones 
With unsupervised segmentation features introduced by BIBREF20, our model gets a higher result  Specially, the results in MSR and AS achieve new state-of-the-art and approaching previous state-of-the-art in CITYU and PKU  The unsupervised segmentation features are derived from the given training dataset, thus using them does not violate the rule of closed test of SIGHAN Bakeoff 
Table TABREF36 compares our model and recent neural models in terms of open test setting in which any external resources, especially pre-trained embeddings or language models can be used  In MSR and AS, our model gets a comparable result while our results in CITYU and PKU are not remarkable 
However, it is well known that it is always hard to compare models when using open test setting, especially with pre-trained embedding  Not all models may use the same method and data to pre-train  Though pre-trained embedding or language model can improve the performance, the performance improvement itself may be from multiple sources  It often that there is a success of pre-trained embedding to improve the performance, while it cannot prove that the model is better 
Compared with other LSTM models, our model performs better in AS and MSR than in CITYU and PKU  Considering the scale of different corpora, we believe that the size of corpus affects our model and the larger size is, the better model performs  For small corpus, the model tends to be overfitting 
Tables TABREF25 and TABREF26 also show the decoding time in different datasets  Our model finishes the segmentation with the least decoding time in all four datasets, thanks to the architecture of model which only takes attention mechanism as basic block 
Related Work ::: Chinese Word Segmentation
CWS is a task for Chinese natural language process to delimit word boundary  BIBREF0 for the first time formulize CWS as a sequence labeling task  BIBREF3 show that different character tag sets can make essential impact for CWS  BIBREF2 use CRFs as a model for CWS, achieving new state-of-the-art  Works of statistical CWS has built the basis for neural CWS 
Neural word segmentation has been widely used to minimize the efforts in feature engineering which was important in statistical CWS  BIBREF4 introduce the neural model with sliding-window based sequence labeling  BIBREF6 propose a gated recursive neural network (GRNN) for CWS to incorporate complicated combination of contextual character and n-gram features  BIBREF7 use LSTM to learn long distance information  BIBREF9 propose a neural framework that eliminates context windows and utilize complete segmentation history  BIBREF33 explore a joint model that performs segmentation, POS-Tagging and chunking simultaneously  BIBREF34 propose a feature-enriched neural model for joint CWS and part-of-speech tagging  BIBREF35 present a joint model to enhance the segmentation of Chinese microtext by performing CWS and informal word detection simultaneously  BIBREF17 propose a character-based convolutional neural model to capture $n$-gram features automatically and an effective approach to incorporate word embeddings  BIBREF11 improve the model in BIBREF9 and propose a greedy neural word segmenter with balanced word and character embedding inputs  BIBREF23 propose a novel neural network model to incorporate unlabeled and partially-labeled data  BIBREF36 propose two methods that extend the Bi-LSTM to perform incorporating dictionaries into neural networks for CWS  BIBREF37 propose Switch-LSTMs to segment words and provided a more flexible solution for multi-criteria CWS which is easy to transfer the learned knowledge to new criteria 
Related Work ::: Transformer
Transformer BIBREF24 is an attention-based neural machine translation model  The Transformer is one kind of self-attention networks (SANs) which is proposed in BIBREF38  Encoder of the Transformer consists of one self-attention layer and a position-wise feed-forward layer  Decoder of the Transformer contains one self-attention layer, one encoder-decoder attention layer and one position-wise feed-forward layer  The Transformer uses residual connections around the sublayers and then followed by a layer normalization layer 
Scaled dot-product attention is the key component in the Transformer  The input of attention contains queries, keys, and values of input sequences  The attention is generated using queries and keys like Equation (DISPLAY_FORM11)  Structure of scaled dot-product attention allows the self-attention layer generate the representation of sentences at once and contain the information of the sentence which is different from RNN that process characters of sentences one by one  Standard self-attention is similar as Gaussian-masked direction attention while it does not have directional mask and gaussian mask  BIBREF24 also propose multi-head attention which is better to generate representation of sentence by dividing queries, keys and values to different heads and get information from different subspaces 
Conclusion
In this paper, we propose an attention mechanism only based Chinese word segmentation model  Our model uses self-attention from the Transformer encoder to take sequence input and bi-affine attention scorer to predict the label of gaps  To improve the ability of capturing the localness and directional information of self-attention based encoder, we propose a variant of self-attention called Gaussian-masked directional multi-head attention to replace the standard self-attention  We also extend the Transformer encoder to capture directional features  Our model uses only unigram features instead of multiple $n$-gram features in previous work  Our model is evaluated on standard benchmark dataset, SIGHAN Bakeoff 2005, which shows not only our model performs segmentation faster than any previous models but also gives new higher or comparable segmentation performance against previous state-of-the-art models 

ground_truth: Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish and Swedish

Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish , Swedish