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 dataset did they use?
Context 1: Introduction
Automatic classification of sentiment has mainly focused on categorizing tweets in either two (binary sentiment analysis) or three (ternary sentiment analysis) categories BIBREF0   In this work we study the problem of fine-grained sentiment classification where tweets are classified according to a five-point scale ranging from VeryNegative to VeryPositive  To illustrate this, Table TABREF3 presents examples of tweets associated with each of these categories  Five-point scales are widely adopted in review sites like Amazon and TripAdvisor, where a user's sentiment is ordered with respect to its intensity  From a sentiment analysis perspective, this defines a classification problem with five categories  In particular, Sebastiani et al  BIBREF1 defined such classification problems whose categories are explicitly ordered to be ordinal classification problems  To account for the ordering of the categories, learners are penalized according to how far from the true class their predictions are 
Although considering different scales, the various settings of sentiment classification are related  First, one may use the same feature extraction and engineering approaches to represent the text spans such as word membership in lexicons, morpho-syntactic statistics like punctuation or elongated word counts BIBREF2 , BIBREF3   Second, one would expect that knowledge from one task can be transfered to the others and this would benefit the performance  Knowing that a tweet is “Positive” in the ternary setting narrows the classification decision between the VeryPositive and Positive categories in the fine-grained setting  From a research perspective this raises the question of whether and how one may benefit when tackling such related tasks and how one can transfer knowledge from one task to another during the training phase 
Our focus in this work is to exploit the relation between the sentiment classification settings and demonstrate the benefits stemming from combining them  To this end, we propose to formulate the different classification problems as a multitask learning problem and jointly learn them  Multitask learning BIBREF4 has shown great potential in various domains and its benefits have been empirically validated BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 using different types of data and learning approaches  An important benefit of multitask learning is that it provides an elegant way to access resources developed for similar tasks  By jointly learning correlated tasks, the amount of usable data increases  For instance, while for ternary classification one can label data using distant supervision with emoticons BIBREF9 , there is no straightforward way to do so for the fine-grained problem  However, the latter can benefit indirectly, if the ternary and fine-grained tasks are learned jointly 
The research question that the paper attempts to answer is the following: Can twitter sentiment classification problems, and fine-grained sentiment classification in particular, benefit from multitask learning? To answer the question, the paper brings the following two main contributions: (i) we show how jointly learning the ternary and fine-grained sentiment classification problems in a multitask setting improves the state-of-the-art performance, and (ii) we demonstrate that recurrent neural networks outperform models previously proposed without access to huge corpora while being flexible to incorporate different sources of data 
Multitask Learning for Twitter Sentiment Classification
In his work, Caruana BIBREF4 proposed a multitask approach in which a learner takes advantage of the multiplicity of interdependent tasks while jointly learning them  The intuition is that if the tasks are correlated, the learner can learn a model jointly for them while taking into account the shared information which is expected to improve its generalization ability  People express their opinions online on various subjects (events, products  ), on several languages and in several styles (tweets, paragraph-sized reviews  ), and it is exactly this variety that motivates the multitask approaches  Specifically for Twitter for instance, the different settings of classification like binary, ternary and fine-grained are correlated since their difference lies in the sentiment granularity of the classes which increases while moving from binary to fine-grained problems 
There are two main decisions to be made in our approach: the learning algorithm, which learns a decision function, and the data representation  With respect to the former, neural networks are particularly suitable as one can design architectures with different properties and arbitrary complexity  Also, as training neural network usually relies on back-propagation of errors, one can have shared parts of the network trained by estimating errors on the joint tasks and others specialized for particular tasks  Concerning the data representation, it strongly depends on the data type available  For the task of sentiment classification of tweets with neural networks, distributed embeddings of words have shown great potential  Embeddings are defined as low-dimensional, dense representations of words that can be obtained in an unsupervised fashion by training on large quantities of text BIBREF10  
Concerning the neural network architecture, we focus on Recurrent Neural Networks (RNNs) that are capable of modeling short-range and long-range dependencies like those exhibited in sequence data of arbitrary length like text  While in the traditional information retrieval paradigm such dependencies are captured using INLINEFORM0 -grams and skip-grams, RNNs learn to capture them automatically BIBREF11   To circumvent the problems with capturing long-range dependencies and preventing gradients from vanishing, the long short-term memory network (LSTM) was proposed BIBREF12   In this work, we use an extended version of LSTM called bidirectional LSTM (biLSTM)  While standard LSTMs access information only from the past (previous words), biLSTMs capture both past and future information effectively BIBREF13 , BIBREF11   They consist of two LSTM networks, for propagating text forward and backwards with the goal being to capture the dependencies better  Indeed, previous work on multitask learning showed the effectiveness of biLSTMs in a variety of problems: BIBREF14 tackled sequence prediction, while BIBREF6 and BIBREF15 used biLSTMs for Named Entity Recognition and dependency parsing respectively 
Figure FIGREF2 presents the architecture we use for multitask learning  In the top-left of the figure a biLSTM network (enclosed by the dashed line) is fed with embeddings INLINEFORM0 that correspond to the INLINEFORM1 words of a tokenized tweet  Notice, as discussed above, the biLSTM consists of two LSTMs that are fed with the word sequence forward and backwards  On top of the biLSTM network one (or more) hidden layers INLINEFORM2 transform its output  The output of INLINEFORM3 is led to the softmax layers for the prediction step  There are INLINEFORM4 softmax layers and each is used for one of the INLINEFORM5 tasks of the multitask setting  In tasks such as sentiment classification, additional features like membership of words in sentiment lexicons or counts of elongated/capitalized words can be used to enrich the representation of tweets before the classification step BIBREF3   The lower part of the network illustrates how such sources of information can be incorporated to the process  A vector “Additional Features” for each tweet is transformed from the hidden layer(s) INLINEFORM6 and then is combined by concatenation with the transformed biLSTM output in the INLINEFORM7 layer 
Experimental setup
Our goal is to demonstrate how multitask learning can be successfully applied on the task of sentiment classification of tweets  The particularities of tweets are to be short and informal text spans  The common use of abbreviations, creative language etc , makes the sentiment classification problem challenging  To validate our hypothesis, that learning the tasks jointly can benefit the performance, we propose an experimental setting where there are data from two different twitter sentiment classification problems: a fine-grained and a ternary  We consider the fine-grained task to be our primary task as it is more challenging and obtaining bigger datasets, e g  by distant supervision, is not straightforward and, hence we report the performance achieved for this task 
Ternary and fine-grained sentiment classification were part of the SemEval-2016 “Sentiment Analysis in Twitter” task BIBREF16   We use the high-quality datasets the challenge organizers released  The dataset for fine-grained classification is split in training, development, development_test and test parts  In the rest, we refer to these splits as train, development and test, where train is composed by the training and the development instances  Table TABREF7 presents an overview of the data  As discussed in BIBREF16 and illustrated in the Table, the fine-grained dataset is highly unbalanced and skewed towards the positive sentiment: only INLINEFORM0 of the training examples are labeled with one of the negative classes 
Feature representation We report results using two different feature sets  The first one, dubbed nbow, is a neural bag-of-words that uses text embeddings to generate low-dimensional, dense representations of the tweets  To construct the nbow representation, given the word embeddings dictionary where each word is associated with a vector, we apply the average compositional function that averages the embeddings of the words that compose a tweet  Simple compositional functions like average were shown to be robust and efficient in previous work BIBREF17   Instead of training embeddings from scratch, we use the pre-trained on tweets GloVe embeddings of BIBREF10   In terms of resources required, using only nbow is efficient as it does not require any domain knowledge  However, previous research on sentiment analysis showed that using extra resources, like sentiment lexicons, can benefit significantly the performance BIBREF3 , BIBREF2   To validate this and examine at which extent neural networks and multitask learning benefit from such features we evaluate the models using an augmented version of nbow, dubbed nbow+  The feature space of the latter, is augmented using 1,368 extra features consisting mostly of counts of punctuation symbols ('!?#@'), emoticons, elongated words and word membership features in several sentiment lexicons  Due to space limitations, for a complete presentation of these features, we refer the interested reader to BIBREF2 , whose open implementation we used to extract them 
Evaluation measure To reproduce the setting of the SemEval challenges BIBREF16 , we optimize our systems using as primary measure the macro-averaged Mean Absolute Error ( INLINEFORM0 ) given by: INLINEFORM1
where INLINEFORM0 is the number of categories, INLINEFORM1 is the set of instances whose true class is INLINEFORM2 , INLINEFORM3 is the true label of the instance INLINEFORM4 and INLINEFORM5 the predicted label  The measure penalizes decisions far from the true ones and is macro-averaged to account for the fact that the data are unbalanced  Complementary to INLINEFORM6 , we report the performance achieved on the micro-averaged INLINEFORM7 measure, which is a commonly used measure for classification 
The models To evaluate the multitask learning approach, we compared it with several other models  Support Vector Machines (SVMs) are maximum margin classification algorithms that have been shown to achieve competitive performance in several text classification problems BIBREF16   SVM INLINEFORM0 stands for an SVM with linear kernel and an one-vs-rest approach for the multi-class problem  Also, SVM INLINEFORM1 is an SVM with linear kernel that employs the crammer-singer strategy BIBREF18 for the multi-class problem  Logistic regression (LR) is another type of linear classification method, with probabilistic motivation  Again, we use two types of Logistic Regression depending on the multi-class strategy: LR INLINEFORM2 that uses an one-vs-rest approach and multinomial Logistic Regression also known as the MaxEnt classifier that uses a multinomial criterion 
Both SVMs and LRs as discussed above treat the problem as a multi-class one, without considering the ordering of the classes  For these four models, we tuned the hyper-parameter INLINEFORM0 that controls the importance of the L INLINEFORM1 regularization part in the optimization problem with grid-search over INLINEFORM2 using 10-fold cross-validation in the union of the training and development data and then retrained the models with the selected values  Also, to account for the unbalanced classification problem we used class weights to penalize more the errors made on the rare classes  These weights were inversely proportional to the frequency of each class  For the four models we used the implementations of Scikit-learn BIBREF19  
For multitask learning we use the architecture shown in Figure FIGREF2 , which we implemented with Keras BIBREF20   The embeddings are initialized with the 50-dimensional GloVe embeddings while the output of the biLSTM network is set to dimension 50  The activation function of the hidden layers is the hyperbolic tangent  The weights of the layers were initialized from a uniform distribution, scaled as described in BIBREF21   We used the Root Mean Square Propagation optimization method  We used dropout for regularizing the network  We trained the network using batches of 128 examples as follows: before selecting the batch, we perform a Bernoulli trial with probability INLINEFORM0 to select the task to train for  With probability INLINEFORM1 we pick a batch for the fine-grained sentiment classification problem, while with probability INLINEFORM2 we pick a batch for the ternary problem  As shown in Figure FIGREF2 , the error is backpropagated until the embeddings, that we fine-tune during the learning process  Notice also that the weights of the network until the layer INLINEFORM3 are shared and therefore affected by both tasks 
To tune the neural network hyper-parameters we used 5-fold cross validation  We tuned the probability INLINEFORM0 of dropout after the hidden layers INLINEFORM1 and for the biLSTM for INLINEFORM2 , the size of the hidden layer INLINEFORM3 and the probability INLINEFORM4 of the Bernoulli trials from INLINEFORM5   During training, we monitor the network's performance on the development set and apply early stopping if the performance on the validation set does not improve for 5 consecutive epochs 
Experimental results Table TABREF9 illustrates the performance of the models for the different data representations  The upper part of the Table summarizes the performance of the baselines  The entry “Balikas et al ” stands for the winning system of the 2016 edition of the challenge BIBREF2 , which to the best of our knowledge holds the state-of-the-art  Due to the stochasticity of training the biLSTM models, we repeat the experiment 10 times and report the average and the standard deviation of the performance achieved 
Several observations can be made from the table  First notice that, overall, the best performance is achieved by the neural network architecture that uses multitask learning  This entails that the system makes use of the available resources efficiently and improves the state-of-the-art performance  In conjunction with the fact that we found the optimal probability INLINEFORM0 , this highlights the benefits of multitask learning over single task learning  Furthermore, as described above, the neural network-based models have only access to the training data as the development are hold for early stopping  On the other hand, the baseline systems were retrained on the union of the train and development sets  Hence, even with fewer resources available for training on the fine-grained problem, the neural networks outperform the baselines  We also highlight the positive effect of the additional features that previous research proposed  Adding the features both in the baselines and in the biLSTM-based architectures improves the INLINEFORM1 scores by several points 
Lastly, we compare the performance of the baseline systems with the performance of the state-of-the-art system of BIBREF2   While BIBREF2 uses n-grams (and character-grams) with INLINEFORM0 , the baseline systems (SVMs, LRs) used in this work use the nbow+ representation, that relies on unigrams  Although they perform on par, the competitive performance of nbow highlights the potential of distributed representations for short-text classification  Further, incorporating structure and distributed representations leads to the gains of the biLSTM network, in the multitask and single task setting 
Similar observations can be drawn from Figure FIGREF10 that presents the INLINEFORM0 scores  Again, the biLSTM network with multitask learning achieves the best performance  It is also to be noted that although the two evaluation measures are correlated in the sense that the ranking of the models is the same, small differences in the INLINEFORM1 have large effect on the scores of the INLINEFORM2 measure 
Conclusion
In this paper, we showed that by jointly learning the tasks of ternary and fine-grained classification with a multitask learning model, one can greatly improve the performance on the second  This opens several avenues for future research  Since sentiment is expressed in different textual types like tweets and paragraph-sized reviews, in different languages (English, German,   ) and in different granularity levels (binary, ternary,  ) one can imagine multitask approaches that could benefit from combining such resources  Also, while we opted for biLSTM networks here, one could use convolutional neural networks or even try to combine different types of networks and tasks to investigate the performance effect of multitask learning  Lastly, while our approach mainly relied on the foundations of BIBREF4 , the internal mechanisms and the theoretical guarantees of multitask learning remain to be better understood 
Acknowledgements
This work is partially supported by the CIFRE N 28/2015 

ground_truth:  high-quality datasets  from SemEval-2016 “Sentiment Analysis in Twitter” task

 SemEval-2016 “Sentiment Analysis in Twitter”

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. Are the automatically constructed datasets subject to quality control?
Context 2: Introduction
Automatically answering questions, especially in the open-domain setting (i e , where minimal or no contextual knowledge is explicitly provided), requires bringing to bear considerable amount of background knowledge and reasoning abilities  For example, knowing the answers to the two questions in Figure FIGREF1 requires identifying a specific ISA relation (i e , that cooking is a type of learned behavior) as well as recalling the definition of a concept (i e , that global warming is defined as a worldwide increase in temperature)  In the multiple-choice setting, which is the variety of question-answering (QA) that we focus on in this paper, there is also pragmatic reasoning involved in selecting optimal answer choices (e g , while greenhouse effect might in some other context be a reasonable answer to the second question in Figure FIGREF1, global warming is a preferable candidate) 
Recent successes in QA, driven largely by the creation of new resources BIBREF2, BIBREF3, BIBREF4, BIBREF5 and advances in model pre-training BIBREF6, BIBREF7, raise a natural question: do state-of-the-art multiple-choice QA (MCQA) models that excel at standard tasks really have basic knowledge and reasoning skills?
Most existing MCQA datasets are constructed through either expensive crowd-sourcing BIBREF8 or hand engineering effort, in the former case making it possible to collect large amounts of data at the cost of losing systematic control over the semantics of the target questions  Hence, doing a controlled experiment to answer such a question for QA is difficult given a lack of targeted challenge datasets 
Having definitive empirical evidence of model competence on any given phenomenon requires constructing a wide range of systematic tests  For example, in measuring competence of definitions, not only do we want to see that the model can handle individual questions such as Figure FIGREF1 1 inside of benchmark tasks, but that it can answer a wider range of questions that exhaustively cover a broad set of concepts and question perturbations (i e , systematic adjustments to how the questions are constructed)  The same applies to ISA reasoning; not only is it important to recognize in the question in Figure FIGREF1 1 that cooking is a learned behavior, but also that cooking is a general type of behavior or, through a few more inferential steps, a type of human activity 
In this paper, we look at systematically constructing such tests by exploiting the vast amounts of structured information contained in various types of expert knowledge such as knowledge graphs and lexical taxonomies  Our general methodology works as illustrated in Figure FIGREF1: given any MCQA model trained on a set of benchmark tasks, we systematically generate a set of synthetic dataset probes (i e , MCQA renderings of the target information) from information in expert knowledge sources  We then use these probes to ask two empirical questions: 1) how well do models trained on benchmark tasks perform on these probing tasks and; 2) can such models be re-trained to master new challenges with minimal performance loss on their original tasks?
While our methodology is amenable to any knowledge source and set of models/benchmark tasks, we focus on probing state-of-the-art transformer models BIBREF7, BIBREF9 in the domain of science MCQA  For sources of expert knowledge, we use WordNet, a comprehensive lexical ontology, and other publicly available dictionary resources  We devise probes that measure model competence in definition and taxonomic knowledge in different settings (including hypernymy, hyponymy, and synonymy detection, and word sense disambiguation)  This choice is motivated by fact that the science domain is considered particularly challenging for QA BIBREF10, BIBREF11, BIBREF12, and existing science benchmarks are known to involve widespread use of such knowledge (see BIBREF1, BIBREF13 for analysis), which is also arguably fundamental to more complex forms of reasoning 
We show that accurately probing QA models via synthetic datasets is not straightforward, as unexpected artifacts can easily arise in such data  This motivates our carefully constructed baselines and close data inspection to ensure probe quality 
Our results confirm that transformer-based QA models have a remarkable ability to recognize certain types of knowledge captured in our probes—even without additional fine-tuning  Such models can even outperform strong task-specific models trained directly on our probing tasks (e g , on definitions, our best model achieves 77% test accuracy without specialized training, as opposed to 51% for a task-specific LSTM-based model)  We also show that the same models can be effectively re-fine-tuned on small samples (even 100 examples) of probe data, and that high performance on the probes tends to correlate with a smaller drop in the model's performance on the original QA task 
Our comprehensive assessment reveals several interesting nuances to the overall positive trend  For example, the performance of even the best QA models degrades substantially on our hyponym probes (by 8-15%) when going from 1-hop links to 2-hops  Further, the accuracy of even our best models on the WordNetQA probe drops by 14-44% under our cluster-based analysis, which assesses whether a model knows several facts about each individual concept, rather than just being good at answering isolated questions  State-of-the-art QA models thus have much room to improve even in some fundamental building blocks, namely definitions and taxonomic hierarchies, of more complex forms of reasoning 
Related Work
We follow recent work on constructing challenge datasets for probing neural models, which has primarily focused on the task of natural language inference (NLI) BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18  Most of this work looks at constructing data through adversarial generation methods, which have also been found useful for creating stronger models BIBREF19  There has also been work on using synthetic data of the type we consider in this paper BIBREF20, BIBREF21, BIBREF22  We closely follow the methodology of BIBREF22, who use hand-constructed linguistic fragments to probe NLI models and study model re-training using a variant of the inoculation by fine-tuning strategy of BIBREF23  In contrast, we focus on probing open-domain MCQA models (see BIBREF24 for a related study in the reading comprehension setting) as well as constructing data from much larger sources of structured knowledge 
Our main study focuses on probing the BERT model and fine-tuning approach of BIBREF7, and other variants thereof, which are all based on the transformer architecture of BIBREF25  Related to our efforts, there have been recent studies into the types of relational knowledge contained in large-scale knowledge models BIBREF26, BIBREF27, BIBREF28, which, similar to our work, probe models using structured knowledge sources  This prior work, however, primarily focuses on unearthing the knowledge contained in the underlying language models as is without further training, using simple (single token) cloze-style probing tasks and templates (similar to what we propose in Section SECREF3)  In contrast, we focus on understanding the knowledge contained in language models after they have been trained for a QA end-task using benchmark datasets in which such knowledge is expected to be widespread  Further, our evaluation is done before and after these models are fine-tuned on our probe QA tasks, using a more complex set of QA templates and target inferences 
The use of lexical resources and knowledge graphs such as WordNet to construct datasets has a long history, and has recently appeared in work on adversarial attacks BIBREF14, BIBREF29 and general task construction BIBREF30, BIBREF31  In the area of MCQA, there is related work on constructing questions from tuples BIBREF32, BIBREF3, both of which involve standard crowd annotation to elicit question-answer pairs (see also BIBREF33, BIBREF34)  In contrast to this work, we focus on generating data in an entirely automatic fashion, which obviates the need for expensive annotation and gives us the flexibility to construct much larger datasets that control a rich set of semantic aspects of the target questions 
Dataset Probes and Construction
Our probing methodology starts by constructing challenge datasets (Figure FIGREF1, yellow box) from a target set of knowledge resources  Each of our probing datasets consists of multiple-choice questions that include a question $\textbf {q}$ and a set of answer choices or candidates $\lbrace a_{1},   a_{N}\rbrace $  This section describes in detail the 5 different datasets we build, which are drawn from two sources of expert knowledge, namely WordNet BIBREF35 and the GNU Collaborative International Dictionary of English (GCIDE)  We describe each resource in turn, and explain how the resulting dataset probes, which we call WordNetQA and DictionaryQA, are constructed 
For convenience, we will describe each source of expert knowledge as a directed, edge-labeled graph $G$  The nodes of this graph are $\mathcal {V} = \mathcal {C} \cup \mathcal {W} \cup \mathcal {S} \cup \mathcal {D}$, where $\mathcal {C}$ is a set of atomic concepts, $\mathcal {W}$ a set of words, $\mathcal {S}$ a set of sentences, and $\mathcal {D}$ a set of definitions (see Table TABREF4 for details for WordNet and GCIDE)  Each edge of $G$ is directed from an atomic concept in $\mathcal {C}$ to another node in $V$, and is labeled with a relation, such as hypernym or isa$^\uparrow $, from a set of relations $\mathcal {R}$ (see Table TABREF4) 
When defining our probe question templates, it will be useful to view $G$ as a set of (relation, source, target) triples $\mathcal {T} \subseteq \mathcal {R} \times \mathcal {C} \times \mathcal {V}$  Due to their origin in an expert knowledge source, such triples preserve semantic consistency  For instance, when the relation in a triple is def, the corresponding edge maps a concept in $\mathcal {C}$ to a definition in $\mathcal {D}$ 
To construct probe datasets, we rely on two heuristic functions, defined below for each individual probe: $\textsc {gen}_{\mathcal {Q}}(\tau )$, which generates gold question-answer pairs $(\textbf {q},\textbf {a})$ from a set of triples $\tau \subseteq \mathcal {T}$ and question templates $\mathcal {Q}$, and $\textsc {distr}(\tau ^{\prime })$, which generates distractor answers choices $\lbrace a^{\prime }_{1},   a^{\prime }_{N-1} \rbrace $ based on another set of triples $\tau ^{\prime }$ (where usually $\tau \subset \tau ^{\prime }$)  For brevity, we will use $\textsc {gen}(\tau )$ to denote $\textsc {gen}_{\mathcal {Q}}(\tau )$, leaving question templates $\mathcal {Q}$ implicit 
Dataset Probes and Construction ::: WordNetQA
WordNet is an English lexical database consisting of around 117k concepts, which are organized into groups of synsets that each contain a gloss (i e , a definition of the target concept), a set of representative English words (called lemmas), and, in around 33k synsets, example sentences  In addition, many synsets have ISA links to other synsets that express complex taxonomic relations  Figure FIGREF6 shows an example and Table TABREF4 summarizes how we formulate WordNet as a set of triples $\mathcal {T}$ of various types  These triples together represent a directed, edge-labeled graph $G$  Our main motivation for using WordNet, as opposed to a resource such as ConceptNet BIBREF36, is the availability of glosses ($\mathcal {D}$) and example sentences ($\mathcal {S}$), which allows us to construct natural language questions that contextualize the types of concepts we want to probe 
Dataset Probes and Construction ::: WordNetQA ::: Example Generation @!START@$\textsc {gen}(\tau )$@!END@ 
We build 4 individual datasets based on semantic relations native to WordNet (see BIBREF37): hypernymy (i e , generalization or ISA reasoning up a taxonomy, ISA$^\uparrow $), hyponymy (ISA$^{\downarrow }$), synonymy, and definitions  To generate a set of questions in each case, we employ a number of rule templates $\mathcal {Q}$ that operate over tuples  A subset of such templates is shown in Table TABREF8  The templates were designed to mimic naturalistic questions we observed in our science benchmarks 
For example, suppose we wish to create a question $\textbf {q}$ about the definition of a target concept $c \in \mathcal {C}$  We first select a question template from $\mathcal {Q}$ that first introduces the concept $c$ and its lemma $l \in \mathcal {W}$ in context using the example sentence $s \in \mathcal {S}$, and then asks to identify the corresponding WordNet gloss $d \in \mathcal {D}$, which serves as the gold answer $\textbf {a}$  The same is done for ISA reasoning; each question about a hypernym/hyponym relation between two concepts $c \rightarrow ^{\uparrow /\downarrow } c^{\prime } \in \mathcal {T}_{i}$ (e g , $\texttt {dog} \rightarrow ^{\uparrow /\downarrow } \texttt {animal/terrier}$) first introduces a context for $c$ and then asks for an answer that identifies $c^{\prime }$ (which is also provided with a gloss so as to contain all available context) 
In the latter case, the rules $(\texttt {isa}^{r},c,c^{\prime }) \in \mathcal {T}_i$ in Table TABREF8 cover only direct ISA links from $c$ in direction $r \in \lbrace \uparrow ,\downarrow \rbrace $  In practice, for each $c$ and direction $r$, we construct tests that cover the set HOPS$(c,r)$ of all direct as well as derived ISA relations of $c$:
This allows us to evaluate the extent to which models are able to handle complex forms of reasoning that require several inferential steps or hops 
Dataset Probes and Construction ::: WordNetQA ::: Distractor Generation: @!START@$\textsc {distr}(\tau ^{\prime })$@!END@ 
An example of how distractors are generated is shown in Figure FIGREF6, which relies on similar principles as above  For each concept $c$, we choose 4 distractor answers that are close in the WordNet semantic space  For example, when constructing hypernymy tests for $c$ from the set hops$(c,\uparrow )$, we build distractors by drawing from $\textsc {hops}(c,\downarrow )$ (and vice versa), as well as from the $\ell $-deep sister family of $c$, defined as follows  The 1-deep sister family is simply $c$'s siblings or sisters, i e , the other children $\tilde{c} \ne c$ of the parent node $c^{\prime }$ of $c$  For $\ell > 1$, the $\ell $-deep sister family also includes all descendants of each $\tilde{c}$ up to $\ell -1$ levels deep, denoted $\textsc {hops}_{\ell -1}(\tilde{c},\downarrow )$  Formally:
For definitions and synonyms we build distractors from all of these sets (with a similar restriction on the depth of sister distractors as noted above)  In doing this, we can systematically investigate model performance on a wide range of distractor sets 
Dataset Probes and Construction ::: WordNetQA ::: Perturbations and Semantic Clusters
Based on how we generate data, for each concept $c$ (i e , atomic WordNet synset) and probe type (i e , definitions, hypernymy, etc ), we have a wide variety of questions related to $c$ that manipulate 1) the complexity of reasoning that is involved (e g , the number of inferential hops) and; 2) the types of distractors (or distractor perturbations) that are employed  We call such sets semantic clusters  As we describe in the next section, semantic clusters allow us to devise new types of evaluation that reveal whether models have comprehensive and consistent knowledge of target concepts (e g , evaluating whether a model can correctly answer several questions associated with a concept, as opposed to a few disjoint instances) 
Details of the individual datasets are shown in Table TABREF12  From these sets, we follow BIBREF22 in allocating a maximum of 3k examples for training and reserve the rest for development and testing  Since we are interested in probing, having large held-out sets allows us to do detailed analysis and cluster-based evaluation 
Dataset Probes and Construction ::: DictionaryQA
The DictionaryQA dataset is created from the GCIDE dictionary, which is a comprehensive open-source English dictionary built largely from the Webster's Revised Unabridged Dictionary BIBREF38  Each entry consists of a word, its part-of-speech, its definition, and an optional example sentence (see Table TABREF14)  Overall, 33k entries (out of a total of 155k) contain example sentences/usages  As with the WordNet probes, we focus on this subset so as to contextualize each word being probed  In contrast to WordNet, GCIDE does not have ISA relations or explicit synsets, so we take each unique entry to be a distinct sense  We then use the dictionary entries to create a probe that centers around word-sense disambiguation, as described below 
Dataset Probes and Construction ::: DictionaryQA ::: Example and Distractor Generation 
To generate gold questions and answers, we use the same generation templates for definitions exemplified in Figure TABREF8 for WordNetQA  To generate distractors, we simply take alternative definitions for the target words that represent a different word sense (e g , the alternative definitions of gift shown in Table TABREF14), as well as randomly chosen definitions if needed to create a 5-way multiple choice question  As above, we reserve a maximum of 3k examples for training  Since we have only 9k examples in total in this dataset (see WordSense in Table TABREF12), we also reserve 3k each for development and testing 
We note that initial attempts to build this dataset through standard random splitting gave rise to certain systematic biases that were exploited by the choice-only baseline models described in the next section, and hence inflated overall model scores  After several efforts at filtering we found that, among other factors, using definitions from entries without example sentences as distractors (e g , the first two entries in Table TABREF14) had a surprising correlation with such biases  This suggests that possible biases involving differences between dictionary entries with and without examples can taint the resulting automatically generated MCQA dataset (for more discussion on the pitfalls involved with automatic dataset construction, see Section SECREF5) 
Probing Methodology and Modeling
Given the probes above, we now can start to answer the empirical questions posed at the beginning  Our main focus is on looking at transformer-based MCQA models trained in the science domain (using the benchmarks shown in Table TABREF21)  In this section, we provide details of MCQA and the target models, as well as several baselines that we use to sanity check our new datasets  To evaluate model competence, we look at a combination of model performance after science pre-training and after additional model fine-tuning using the lossless inoculation strategy of BIBREF22 (Section SECREF22)  In Section SECREF24, we also discuss a cluster-level accuracy metric for measuring performance over semantic clusters 
Probing Methodology and Modeling ::: Task Definition and Modeling
Given a dataset $D =\lbrace (\textbf {q}^{(d)}, \lbrace a_{1}^{(d)},   , a_{N}^{(d)}\rbrace ) \rbrace _{d}^{\mid D \mid }$ consisting of pairs of questions stems $\textbf {q}$ and answer choices $a_{i}$, the goal is to find the correct answer $a_{i^{*}}$ that correctly answers each $\textbf {q}$  Throughout this paper, we look at 5-way multiple-choice problems (i e , where each $N=5$) 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Question+Answer Encoder 
To model this, our investigation centers around the use of the transformer-based BIBREF25 BERT encoder and fine-tuning approach of BIBREF7 (see also BIBREF6)  For each question and individual answer pair $q^{(j)}_{a_{i}}$, we assume the following rendering of this input:
which is run through the pre-trained BERT encoder to generate a representation for $ q^{(j)}_{a_{i}}$ using the hidden state representation for CLS (i e , the classifier token) $\textbf {c}_{i}$:
The probability of a given answer $p^{(j)}_{i}$ is then computed as $p^{(j)}_{i} \propto e^{\textbf {v}\cdot \textbf {c}^{(j)}_{i}}$, which uses an additional set of classification parameters $\textbf {v} \in \mathbb {R}^{H}$ that are optimized (along with the full transformer network) by taking the final loss of the probability of each correct answer $p_{i^{*}}$ over all answer choices:
We specifically use BERT-large uncased with whole-word masking, as well as the RoBERTa-large model from BIBREF9, which is a more robustly trained version of the original BERT model  Our system uses the implementations provided in AllenNLP BIBREF39 and Huggingface BIBREF40 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks 
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42)  To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models 
Following the notation from BIBREF0, for any given sequence $s$ of tokens in $\lbrace q^{(j)}, a_{1}^{(j)},   ,a_{N}^{(j)}\rbrace $ in $D$, an encoding of $s$ is given as $h_{s}^{(j)} = \textbf {BiLSTM}(\textsc {embed}(s)) \in \mathbb {R}^{|s| \times 2h}$ (where $h$ is the dimension of the hidden state in each directional network, and embed$(\cdot )$ is an embedding function that assigns token-level embeddings to each token in $s$)  A contextual representation for each $s$ is then built by applying an element-wise max operation over $h_{s}$ as follows:
With these contextual representations, different baseline models can be constructed  For example, a Choice-Only model, which is a variant of the well-known hypothesis-only baseline used in NLI BIBREF46, scores each choice $c_{i}$ in the following way:
for $\textbf {W}^{T} \in \mathbb {R}^{2h}$ independently of the question and assigns a probability to each answer $p_{i}^{(j)} \propto e^{\alpha _{i}^{(j)}}$ 
A slight variant of this model, the Choice-to-choice model, tries to single out a given answer choice relative to other choices by scoring all choice pairs $\alpha _{i,i^{\prime }}^{(j)} = \textsc {Att}(r^{(j)}_{c_{i}},r^{(j)}_{c_{i^{\prime }}}) \in \mathbb {R}$ using a learned attention mechanism Att and finding the choice with the minimal similarity to other options (for full details, see their original paper)  In using these partial-input baselines, which we train directly on each target probe, we can check whether systematic biases related to answer choices were introduced into the data creation process 
A Question-to-choice model, in contrast, uses the contextual representations for each question and individual choice and an attention model Att model to get a score $\alpha ^{(j)}_{q,i} = \textsc {Att}(r^{(j)}_{q},r^{(j)}_{c_{i}}) \in \mathbb {R}$ as above  Here we also experiment with using ESIM BIBREF47 to generate the contextual representations $r$, as well as a simpler VecSimilarity model that measures the average vector similarity between question and answer tokens: $\alpha ^{(j)}_{q,i} = \textsc {Sim}(\textsc {embed}(q^{(j)}),\textsc {embed}(c^{(j)}_{i}))$  In contrast to the models above, these sets of baselines are used to check for artifacts between questions and answers that are not captured in the partial-input baselines (see discussion in BIBREF49) and ensure that the overall MCQA tasks are sufficiently difficult for our transformer models 
Probing Methodology and Modeling ::: Inoculation and Pre-training
Using the various models introduced above, we train these models on benchmark tasks in the science domain and look at model performance on our probes with and without additional training on samples of probe data, building on the idea of inoculation from BIBREF23  Model inoculation is the idea of continuing to train models on new challenge tasks (in our cases, separately for each probe) using only a small amount of examples  Unlike in ordinary fine-tuning, the goal is not to learn an entirely re-purposed model, but to improve on (or vaccinate against) particular phenomena (e g , our synthetic probes) that potentially deviate from a model's original training distribution (but that nonetheless might involve knowledge already contained in the model) 
In the variant proposed in BIBREF22, for each pre-trained (science) model and architecture $M_{a}$ we continue training the model on $k$ new probe examples (with a maximum of $k=$ 3k) under a set of different hyper-parameter configurations $j \in \lbrace 1,    , J\rbrace $ and identify, for each $k$, the model $M_{*}^{a,k}$ with the best aggregate performance $S$ on the original (orig) and new task:
As in BIBREF22, we found all models to be especially sensitive to different learning rates, and performed comprehensive hyper-parameters searches that also manipulate the number of iterations and random seeds used 
Using this methodology, we can see how much exposure to new data it takes for a given model to master a new task, and whether there are phenomena that stress particular models (e g , lead to catastrophic forgetting of the original task)  Given the restrictions on the number of fine-tuning examples, our assumption is that when models are able to maintain good performance on their original task during inoculation, the quickness with which they are able to learn the inoculated task provides evidence of prior competence, which is precisely what we aim to probe  To measure past performance, we define a model's inoculation cost as the difference in the performance of this model on its original task before and after inoculation 
We pre-train on an aggregated training set of the benchmark science exams detailed in Table TABREF21, and created an aggregate development set of around 4k science questions for evaluating overall science performance and inoculation costs  To handle the mismatch between number of answer choices in these sets, we made all sets 5-way by adding empty answers as needed  We also experimented with a slight variant of inoculation, called add-some inoculation, which involves balancing the inoculation training sets with naturalistic science questions  We reserve the MCQL dataset in Table TABREF21 for this purpose, and experiment with balancing each probe example with a science example (x1 matching) and adding twice as many science questions (x2 matching, up to 3k) for each new example 
Probing Methodology and Modeling ::: Evaluating Model Competence
The standard way to evaluate our MCQA models is by looking at the overall accuracy of the correct answer prediction, or what we call instance-level accuracy (as in Table TABREF25)  Given the nature of our data and the existence of semantic clusters as detailed in Section SECREF11 (i e , sets of questions and answers under different distractor choices and inference complexity), we also measure a model's cluster-level (or strict cluster) accuracy, which requires correctly answering all questions in a cluster  Example semantic clusters are shown in Table TABREF30; in the first case, there are 6 ISA$^\uparrow $ questions (including perturbations) about the concept trouser n 01 (e g , involving knowing that trousers are a type of consumer good and garment/clothing), which a model must answer in order to receive full credit 
Our cluster-based analysis is motivated by the idea that if a model truly knows the meaning of a given concept, such as the concept of trousers, then it should be able to answer arbitrary questions about this concept without sensitivity to varied distractors  While our strict cluster metric is simplistic, it takes inspiration from work on visual QA BIBREF53, and allows us to evaluate how consistent and robust models are across our different probes, and to get insight into whether errors are concentrated on a small set of concepts or widespread across clusters 
Results and Findings
In this section, we provide the results of the empirical questions first introduced in Figure FIGREF1, starting with the results of our baseline models 
Results and Findings ::: Are our Probes Sufficiently Challenging?
As shown in Table TABREF25, most of our partial-input baselines (i e , Choice-Only and Choice-to-Choice models) failed to perform well on our dataset probes across a wide range of models, showing that such probes are generally immune from biases relating to how distractors were generated  As already discussed in Section SECREF13, however, initial versions of the DictionaryQA dataset had unforeseen biases partly related to whether distractors were sampled from entries without example sentences, which resulted in high Choice-Only-GloVe scores ranging around 56% accuracy before a filtering step was applied to remove these distractors 
We had similar issues with the hypernymy probe which, even after a filtering step that used our Choice-to-Choice-GloVe model, still leads to high results on the BERT and RoBERTa choice-only models  Given that several attempts were made to entirely de-duplicate the different splits (both in terms of gold answers and distractor types), the source of these biases is not at all obvious, which shows how easy it is for unintended biases in expert knowledge to appear in the resulting datasets and the importance of having rigorous baselines  We also note the large gap in some cases between the BERT and RoBERTa versus GloVe choice-only models, which highlights the need for having partial-input baselines that use the best available models 
Using a more conventional set of Task-Specific QA models (i e , the LSTM-based Question-to-Choice models trained directly on the probes), we can see that results are not particularly strong on any of the datasets, suggesting that our probes are indeed sufficiently challenging and largely immune from overt artifacts  The poor performance of the VecSimilarity (which uses pre-trained Word2Vec embeddings without additional training) provides additional evidence that elementary lexical matching strategies are insufficient for solving any of the probing tasks 
Results and Findings ::: How well do pre-trained MCQA models do?
Science models that use non-transformer based encoders, such as the ESIM model with GloVe and ELMO, perform poorly across all probes, in many cases scoring near random chance, showing limits to how well they generalize from science to other tasks even with pre-trained GloVe and ELMO embeddings  In sharp contrast, the transformer models have mixed results, the most striking result being the RoBERTa models on the definitions and synonymy probes (achieving a test accuracy of 77% and 61%, respectively), which outperform several of the task-specific LSTM models trained directly on the probes  At first glance, this suggests that RoBERTa, which generally far outpaces even BERT across most probes, has high competence of definitions and synonyms even without explicit training on our new tasks 
Given the controlled nature of our probes, we can get a more detailed view of how well the science models are performing across different reasoning and distractor types, as shown in the first column of Figure FIGREF28 for ESIM and RoBERTa  The ESIM science model without training has uniformly poor performance across all categories, whereas the performance of RoBERTa is more varied  Across all datasets and number of hops (i e , the rows in the heat maps), model performance for RoBERTa is consistently highest among examples with random distractors (i e , the first column), and lowest in cases involving distractors that are closest in WordNet space (e g , sister and ISA, or up/down, distractors of distance $k^{\prime }=1$)  This is not surprising, given that, in the first case, random distractors are likely to be the easiest category (and the opposite for distractors close in space), but suggests that RoBERTa might only be getting the easiest cases correct 
Model performance also clearly degrades for hypernymy and hyponymy across all models as the number of hops $k$ increases (see red dashed boxes)  For example, problems that involve hyponym reasoning with sister distractors of distance $k^{\prime }=1$ (i e , the second column) degrades from 47% to 15% when the number of hops $k$ increases from 1 to 4  This general tendency persists even after additional fine-tuning, as we discuss next, and gives evidence that models are limited in their capacity for certain types of multi-hop inferences 
As discussed by BIBREF26, the choice of generation templates can have a significant effect on model performance  The results so far should therefore be regarded as a lower bound on model competence  It is possible that model performance is high for definitions, for example, because the associated templates best align with the science training distribution (which we know little about)  For this reason, the subsequent inoculation step is important—it gives the model an opportunity to learn about our target templates and couple this learned knowledge with its general knowledge acquired during pre-training and science training (which is, again, what we aim to probe) 
Results and Findings ::: Can Models Be Effectively Inoculated?
Model performance after additional fine-tuning, or inoculation, is shown in the last 3 rows of Table TABREF25, along with learning curves shown in Figure FIGREF29 for a selection of probes and models  In the former case, the performance represents the model (and inoculation amount) with the highest aggregate performance over the old task and new probe  Here we again see the transformer-based models outperform non-transformer models, and that better models correlate with lower inoculation costs  For example, when inoculating on synonymy, the cost for ESIM is around 7% reduced accuracy on its original task, as opposed to $< 1$% and around 1% for BERT and RoBERTa, respectively  This shows the high capacity for transformer models to absorb new tasks with minimal costs, as also observed in BIBREF22 for NLI 
As shown in Figure FIGREF29, transformer models tend to learn most tasks fairly quickly while keeping constant scores on their original tasks (i e , the flat dashed lines observed in plots 1-4), which gives evidence of high competence  In both cases, add-some inoculation proves to be a cheap and easy way to 1) improve scores on the probing tasks (i e , the solid black and blue lines in plot 1) and; 2) minimize loss on science (e g , the blue and black dashed lines in plots 2-4)  The opposite is the case for ESIM (plots 5-6); models are generally unable to simultaneously learn individual probes without degrading on their original task, and adding more science data during inoculation confuses models on both tasks 
As shown in Figure FIGREF28, RoBERTa is able to significantly improve performance across most categories even after inoculation with a mere 100 examples (the middle plot), which again provides strong evidence of prior competence  As an example, RoBERTa improves on 2-hop hyponymy inference with random distractors by 18% (from 59% to 77%)  After 3k examples, the model has high performance on virtually all categories (the same score increases from 59% to 87%), however results still tends to degrade as a function of hop and distractor complexity, as discussed above 
Despite the high performance of our transformer models after inoculation, model performance on most probes (with the exception of Definitions) averages around 80% for our best models  This suggests that there is still considerable room for improvement, especially for synonymy and word sense, which is a topic that we discuss more in Section SECREF6 
Results and Findings ::: Are Models Consistent across Clusters?
Table TABREF32 shows cluster-level accuracies for the different WordNetQA probes  As with performance across the different inference/distractor categories, these results are mixed  For some probes, such as definitions, our best models appear to be rather robust; e g , our RoBERTa model has a cluster accuracy of $75\%$, meaning that it can answer all questions perfectly for 75% of the target concepts and that errors are concentrated on a small minority (25%) of concepts  On synonymy and hypernymy, both BERT and RoBERTa appear robust on the majority of concepts, showing that errors are similarly concentrated  In contrast, our best model on hyponymy has an accuracy of 36%, meaning that its errors are spread across many concepts, thus suggesting less robustness 
Table TABREF30 shows a selection of semantic clusters involving ISA reasoning, as well as the model performance over different answers (shown symbolically) and perturbations  For example, in the the second case, the cluster is based around the concept/synset oppose v 06 and involves 4 inferences and a total 24 questions (i e , inferences with perturbations)  Our weakest model, ESIM, answers only 5 out of 24 questions correctly, whereas RoBERTa gets 21/24  In the other cases, RoBERTa gets all clusters correct, whereas BERT and ESIM get none of them correct 
We emphasize that these results only provide a crude look into model consistency and robustness  Recalling again the details in Table TABREF12, probes differ in terms of average size of clusters  Hyponymy, in virtue of having many more questions per cluster, might simply be a much more difficult dataset  In addition, such a strict evaluation does not take into account potential errors inside of clusters, which is an important issue that we discuss in the next section  We leave addressing such issues and coming up with more insightful cluster-based metrics for future work 
Discussion and Conclusion
We presented several new challenge datasets and a novel methodology for automatically building such datasets from knowledge graphs and taxonomies  We used these to probe state-of-the-art open-domain QA models (centering around models based on variants of BERT)  While our general methodology is amendable to any target knowledge resource or QA model/domain, we focus on probing definitions and ISA knowledge using open-source dictionaries and MCQA models trained in the science domain 
We find, consistent with recent probing studies BIBREF26, that transformer-based models have a remarkable ability to answer questions that involve complex forms of relational knowledge, both with and without explicit exposure to our new target tasks  In the latter case, a newer RoBERTa model trained only on benchmark science tasks is able to outperform several task-specific LSTM-based models trained directly on our probing data  When re-trained on small samples (e g , 100 examples) of probing data using variations of the lossless inoculation strategy from BIBREF22, RoBERTa is able to master many aspects of our probes with virtually no performance loss on its original QA task 
These positive results suggest that transformer-based models, especially models additionally fine-tuned on small samples of synthetic data, can be used in place of task-specific models used for querying relational knowledge, as has already been done for targeted tasks such as word sense disambiguation BIBREF54  Since models seem to already contain considerable amounts of relational knowledge, our simple inoculation strategy, which tries to nudge models to bring out this knowledge explicitly, could serve as a cheaper alternative to recent attempts to build architectures that explicitly incorporate structured knowledge BIBREF55; we see many areas where our inoculation strategy could be improved for such purposes, including having more complex loss functions that manage old and new information, as well as using techniques that take into account network plasticity BIBREF56 
The main appeal of using automatically generate datasets is the ability to systematically manipulate and control the complexity of target questions, which allows for more controlled experimentation and new forms of evaluation  Despite the positive results described above, results that look directly at the effect of different types of distractors and the complexity of reasoning show that our best models, even after additional fine-tuning, struggle with certain categories of hard distractors and multi-hop inferences  For some probes, our cluster-based analysis also reveals that errors are widespread across concept clusters, suggesting that models are not always consistent and robust  These results, taken together with our findings about the vulnerability of synthetic datasets to systematic biases, suggest that there is much room for improvement and that the positive results should be taken with a grain of salt  Developing better ways to evaluate semantic clusters and model robustness would be a step in this direction 
We emphasize that using synthetic versus naturalistic QA data comes with important trade-offs  While we are able to generate large amounts of systematically controlled data at virtually no cost or need for manual annotation, it is much harder to validate the quality of such data at such a scale and such varying levels of complexity  Conversely, with benchmark QA datasets, it is much harder to perform the type of careful manipulations and cluster-based analyses we report here  While we assume that the expert knowledge we employ, in virtue of being hand-curated by human experts, is generally correct, we know that such resources are fallible and error-prone  Initial crowd-sourcing experiments that look at validating samples of our data show high agreement across probes and that human scores correlate with the model trends across the probe categories  More details of these studies are left for future work 
Context 3: Introduction
Data annotation is a major bottleneck for the application of supervised learning approaches to many problems  As a result, unsupervised methods that learn directly from unlabeled data are increasingly important  For tasks related to unsupervised syntactic analysis, discrete generative models have dominated in recent years – for example, for both part-of-speech (POS) induction BIBREF0 , BIBREF1 and unsupervised dependency parsing BIBREF2 , BIBREF3 , BIBREF4   While similar models have had success on a range of unsupervised tasks, they have mostly ignored the apparent utility of continuous word representations evident from supervised NLP applications BIBREF5 , BIBREF6   In this work, we focus on leveraging and explicitly representing continuous word embeddings within unsupervised models of syntactic structure 
Pre-trained word embeddings from massive unlabeled corpora offer a compact way of injecting a prior notion of word similarity into models that would otherwise treat words as discrete, isolated categories  However, the specific properties of language captured by any particular embedding scheme can be difficult to control, and, further, may not be ideally suited to the task at hand  For example, pre-trained skip-gram embeddings BIBREF7 with small context window size are found to capture the syntactic properties of language well BIBREF8 , BIBREF9   However, if our goal is to separate syntactic categories, this embedding space is not ideal – POS categories correspond to overlapping interspersed regions in the embedding space, evident in Figure SECREF4  
In our approach, we propose to learn a new latent embedding space as a projection of pre-trained embeddings (depicted in Figure SECREF5 ), while jointly learning latent syntactic structure – for example, POS categories or syntactic dependencies  To this end, we introduce a new generative model (shown in Figure FIGREF6 ) that first generates a latent syntactic representation (e g  a dependency parse) from a discrete structured prior (which we also call the “syntax model”), then, conditioned on this representation, generates a sequence of latent embedding random variables corresponding to each word, and finally produces the observed (pre-trained) word embeddings by projecting these latent vectors through a parameterized non-linear function  The latent embeddings can be jointly learned with the structured syntax model in a completely unsupervised fashion 
By choosing an invertible neural network as our non-linear projector, and then parameterizing our model in terms of the projection's inverse, we are able to derive tractable exact inference and marginal likelihood computation procedures so long as inference is tractable in the underlying syntax model  In sec:learn-with-inv we show that this derivation corresponds to an alternate view of our approach whereby we jointly learn a mapping of observed word embeddings to a new embedding space that is more suitable for the syntax model, but include an additional Jacobian regularization term to prevent information loss 
Recent work has sought to take advantage of word embeddings in unsupervised generative models with alternate approaches BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12   BIBREF9 build an HMM with Gaussian emissions on observed word embeddings, but they do not attempt to learn new embeddings  BIBREF10 , BIBREF11 , and BIBREF12 extend HMM or dependency model with valence (DMV) BIBREF2 with multinomials that use word (or tag) embeddings in their parameterization  However, they do not represent the embeddings as latent variables 
In experiments, we instantiate our approach using both a Markov-structured syntax model and a tree-structured syntax model – specifically, the DMV  We evaluate on two tasks: part-of-speech (POS) induction and unsupervised dependency parsing without gold POS tags  Experimental results on the Penn Treebank BIBREF13 demonstrate that our approach improves the basic HMM and DMV by a large margin, leading to the state-of-the-art results on POS induction, and state-of-the-art results on unsupervised dependency parsing in the difficult training scenario where neither gold POS annotation nor punctuation-based constraints are available 
Model
As an illustrative example, we first present a baseline model for Markov syntactic structure (POS induction) that treats a sequence of pre-trained word embeddings as observations  Then, we propose our novel approach, again using Markov structure, that introduces latent word embedding variables and a neural projector  Lastly, we extend our approach to more general syntactic structures 
Example: Gaussian HMM
We start by describing the Gaussian hidden Markov model introduced by BIBREF9 , which is a locally normalized model with multinomial transitions and Gaussian emissions  Given a sentence of length INLINEFORM0 , we denote the latent POS tags as INLINEFORM1 , observed (pre-trained) word embeddings as INLINEFORM2 , transition parameters as INLINEFORM3 , and Gaussian emission parameters as INLINEFORM4   The joint distribution of data and latent variables factors as:
DISPLAYFORM0
where INLINEFORM0 is the multinomial transition probability and INLINEFORM1 is the multivariate Gaussian emission probability 
While the observed word embeddings do inform this model with a notion of word similarity – lacking in the basic multinomial HMM – the Gaussian emissions may not be sufficiently flexible to separate some syntactic categories in the complex pre-trained embedding space – for example the skip-gram embedding space as visualized in Figure SECREF4 where different POS categories overlap  Next we introduce a new approach that adds flexibility to the emission distribution by incorporating new latent embedding variables 
Markov Structure with Neural Projector
To flexibly model observed embeddings and yield a new representation space that is more suitable for the syntax model, we propose to cascade a neural network as a projection function, deterministically transforming the simple space defined by the Gaussian HMM to the observed embedding space  We denote the latent embedding of the INLINEFORM0 word in a sentence as INLINEFORM1 , and the neural projection function as INLINEFORM2 , parameterized by INLINEFORM3   In the case of sequential Markov structure, our new model corresponds to the following generative process:
For each time step INLINEFORM0 ,
[noitemsep, leftmargin=*]
Draw the latent state INLINEFORM0
Draw the latent embedding INLINEFORM0
Deterministically produce embedding
INLINEFORM0
The graphical model is depicted in Figure FIGREF6   The deterministic projection can also be viewed as sampling each observation from a point mass at INLINEFORM0   The joint distribution of our model is: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, and INLINEFORM1 is the Dirac delta function centered at INLINEFORM2 : DISPLAYFORM0
General Structure with Neural Projector
Our approach can be applied to a broad family of structured syntax models  We denote latent embedding variables as INLINEFORM0 , discrete latent variables in the syntax model as INLINEFORM1 ( INLINEFORM2 ), where INLINEFORM3 are conditioned to generate INLINEFORM4   The joint probability of our model factors as:
DISPLAYFORM0
where INLINEFORM0 represents the probability of the syntax model, and can encode any syntactic structure – though, its factorization structure will determine whether inference is tractable in our full model  As shown in Figure FIGREF6 , we focus on two syntax models for syntactic analysis in this paper  The first is Markov-structured, which we use for POS induction, and the second is DMV-structured, which we use to learn dependency parses without supervision 
The marginal data likelihood of our model is: DISPLAYFORM0
While the discrete variables INLINEFORM0 can be marginalized out with dynamic program in many cases, it is generally intractable to marginalize out the latent continuous variables, INLINEFORM1 , for an arbitrary projection INLINEFORM2 in Eq  ( EQREF17 ), which means inference and learning may be difficult  In sec:opt, we address this issue by constraining INLINEFORM3 to be invertible, and show that this constraint enables tractable exact inference and marginal likelihood computation 
Learning & Inference
In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge  Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists  Invertible transformations have been explored before in independent components analysis BIBREF14 , gaussianization BIBREF15 , and deep density models BIBREF16 , BIBREF17 , BIBREF18 , for unstructured data  Here, we generalize this style of approach to structured learning, and augment it with discrete latent variables ( INLINEFORM2 )  Under the invertibility condition, we derive a learning algorithm and give another view of our approach revealed by the objective function  Then, we present the architecture of a neural projector we use in experiments: a volume-preserving invertible neural network proposed by BIBREF16 for independent components estimation 
Learning with Invertibility
For ease of exposition, we explain the learning algorithm in terms of Markov structure without loss of generality  As shown in Eq  ( EQREF17 ), the optimization challenge in our approach comes from the intractability of the marginalized emission factor INLINEFORM0   If we can marginalize out INLINEFORM1 and compute INLINEFORM2 , then the posterior and marginal likelihood of our Markov-structured model can be computed with the forward-backward algorithm  We can apply Eq  ( EQREF14 ) and obtain : INLINEFORM3
By using the change of variable rule to the integration, which allows the integration variable INLINEFORM0 to be replaced by INLINEFORM1 , the marginal emission factor can be computed in closed-form when the invertibility condition is satisfied: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, INLINEFORM1 is the Jacobian matrix of function INLINEFORM2 at INLINEFORM3 , and INLINEFORM4 represents the absolute value of its determinant  This Jacobian term is nonzero and differentiable if and only if INLINEFORM5 exists 
Eq  ( EQREF19 ) shows that we can directly calculate the marginal emission distribution INLINEFORM0   Denote the marginal data likelihood of Gaussian HMM as INLINEFORM1 , then the log marginal data likelihood of our model can be directly written as: DISPLAYFORM0
where INLINEFORM0 represents the new sequence of embeddings after applying INLINEFORM1 to each INLINEFORM2   Eq  ( EQREF20 ) shows that the training objective of our model is simply the Gaussian HMM log likelihood with an additional Jacobian regularization term  From this view, our approach can be seen as equivalent to reversely projecting the data through INLINEFORM3 to another manifold INLINEFORM4 that is directly modeled by the Gaussian HMM, with a regularization term  Intuitively, we optimize the reverse projection INLINEFORM5 to modify the INLINEFORM6 space, making it more appropriate for the syntax model  The Jacobian regularization term accounts for the volume expansion or contraction behavior of the projection  Maximizing it can be thought of as preventing information loss  In the extreme case, the Jacobian determinant is equal to zero, which means the projection is non-invertible and thus information is being lost through the projection  Such “information preserving” regularization is crucial during optimization, otherwise the trivial solution of always projecting data to the same single point to maximize likelihood is viable 
More generally, for an arbitrary syntax model the data likelihood of our approach is: DISPLAYFORM0
If the syntax model itself allows for tractable inference and marginal likelihood computation, the same dynamic program can be used to marginalize out INLINEFORM0   Therefore, our joint model inherits the tractability of the underlying syntax model 
Invertible Volume-Preserving Neural Net
For the projection we can use an arbitrary invertible function, and given the representational power of neural networks they seem a natural choice  However, calculating the inverse and Jacobian of an arbitrary neural network can be difficult, as it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive  To address this issue, several recent papers propose specially designed invertible networks that are easily trainable yet still powerful BIBREF16 , BIBREF17 , BIBREF19   Inspired by these works, we use the invertible transformation proposed by BIBREF16 , which consists of a series of “coupling layers”  This architecture is specially designed to guarantee a unit Jacobian determinant (and thus the invertibility property) 
From Eq  ( EQREF22 ) we know that only INLINEFORM0 is required for accomplishing learning and inference; we never need to explicitly construct INLINEFORM1   Thus, we directly define the architecture of INLINEFORM2   As shown in Figure FIGREF24 , the nonlinear transformation from the observed embedding INLINEFORM3 to INLINEFORM4 represents the first coupling layer  The input in this layer is partitioned into left and right halves of dimensions, INLINEFORM5 and INLINEFORM6 , respectively  A single coupling layer is defined as: DISPLAYFORM0
where INLINEFORM0 is the coupling function and can be any nonlinear form  This transformation satisfies INLINEFORM1 , and BIBREF16 show that its Jacobian matrix is triangular with all ones on the main diagonal  Thus the Jacobian determinant is always equal to one (i e  volume-preserving) and the invertibility condition is naturally satisfied 
To be sufficiently expressive, we compose multiple coupling layers as suggested in BIBREF16   Specifically, we exchange the role of left and right half vectors at each layer as shown in Figure FIGREF24   For instance, from INLINEFORM0 to INLINEFORM1 the left subset INLINEFORM2 is unchanged, while from INLINEFORM3 to INLINEFORM4 the right subset INLINEFORM5 remains the same  Also note that composing multiple coupling layers does not change the volume-preserving and invertibility properties  Such a sequence of invertible transformations from the data space INLINEFORM6 to INLINEFORM7 is also called normalizing flow BIBREF20  
Experiments
In this section, we first describe our datasets and experimental setup  We then instantiate our approach with Markov and DMV-structured syntax models, and report results on POS tagging and dependency grammar induction respectively  Lastly, we analyze the learned latent embeddings 
Data
For both POS tagging and dependency parsing, we run experiments on the Wall Street Journal (WSJ) portion of the Penn Treebank  To create the observed data embeddings, we train skip-gram word embeddings BIBREF7 that are found to capture syntactic properties well when trained with small context window BIBREF8 , BIBREF9   Following BIBREF9 , the dimensionality INLINEFORM0 is set to 100, and the training context window size is set to 1 to encode more syntactic information  The skip-gram embeddings are trained on the one billion word language modeling benchmark dataset BIBREF21 in addition to the WSJ corpus 
General Experimental Setup
For the neural projector, we employ rectified networks as coupling function INLINEFORM0 following BIBREF16   We use a rectified network with an input layer, one hidden layer, and linear output units, the number of hidden units is set to the same as the number of input units  The number of coupling layers are varied as 4, 8, 16 for both tasks  We optimize marginal data likelihood directly using Adam BIBREF22   For both tasks in the fully unsupervised setting, we do not tune the hyper-parameters using supervised data 
Unsupervised POS tagging
For unsupervised POS tagging, we use a Markov-structured syntax model in our approach, which is a popular structure for unsupervised tagging tasks BIBREF9 , BIBREF10  
Following existing literature, we train and test on the entire WSJ corpus (49208 sentences, 1M tokens)  We use 45 tag clusters, the number of POS tags that appear in WSJ corpus  We train the discrete HMM and the Gaussian HMM BIBREF9 as baselines  For the Gaussian HMM, mean vectors of Gaussian emissions are initialized with the empirical mean of all word vectors with an additive noise  We assume diagonal covariance matrix for INLINEFORM0 and initialize it with the empirical variance of the word vectors  Following BIBREF9 , the covariance matrix is fixed during training  The multinomial probabilities are initialized as INLINEFORM1 , where INLINEFORM2   For our approach, we initialize the syntax model and Gaussian parameters with the pre-trained Gaussian HMM  The weights of layers in the rectified network are initialized from a uniform distribution with mean zero and a standard deviation of INLINEFORM3 , where INLINEFORM4 is the input dimension  We evaluate the performance of POS tagging with both Many-to-One (M-1) accuracy BIBREF23 and V-Measure (VM) BIBREF24   Given a model we found that the tagging performance is well-correlated with the training data likelihood, thus we use training data likelihood as a unsupervised criterion to select the trained model over 10 random restarts after training 50 epochs  We repeat this process 5 times and report the mean and standard deviation of performance 
We compare our approach with basic HMM, Gaussian HMM, and several state-of-the-art systems, including sophisticated HMM variants and clustering techniques with hand-engineered features  The results are presented in Table TABREF32   Through the introduced latent embeddings and additional neural projection, our approach improves over the Gaussian HMM by 5 4 points in M-1 and 5 6 points in VM  Neural HMM (NHMM) BIBREF10 is a baseline that also learns word representation jointly  Both their basic model and extended Conv version does not outperform the Gaussian HMM  Their best model incorporates another LSTM to model long distance dependency and breaks the Markov assumption, yet our approach still achieves substantial improvement over it without considering more context information  Moreover, our method outperforms the best published result that benefits from hand-engineered features BIBREF27 by 2 0 points on VM 
We found that most tagging errors happen in noun subcategories  Therefore, we do the one-to-one mapping between gold POS tags and induced clusters and plot the normalized confusion matrix of noun subcategories in Figure FIGREF35   The Gaussian HMM fails to identify “NN” and “NNS” correctly for most cases, and it often recognizes “NNPS” as “NNP”  In contrast, our approach corrects these errors well 
Unsupervised Dependency Parsing without gold POS tags
For the task of unsupervised dependency parse induction, we employ the Dependency Model with Valence (DMV) BIBREF2 as the syntax model in our approach  DMV is a generative model that defines a probability distribution over dependency parse trees and syntactic categories, generating tokens and dependencies in a head-outward fashion  While, traditionally, DMV is trained using gold POS tags as observed syntactic categories, in our approach, we treat each tag as a latent variable, as described in sec:general-neural 
Most existing approaches to this task are not fully unsupervised since they rely on gold POS tags following the original experimental setup for DMV  This is partially because automatically parsing from words is difficult even when using unsupervised syntactic categories BIBREF29   However, inducing dependencies from words alone represents a more realistic experimental condition since gold POS tags are often unavailable in practice  Previous work that has trained from words alone often requires additional linguistic constraints (like sentence internal boundaries) BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , acoustic cues BIBREF33 , additional training data BIBREF4 , or annotated data from related languages BIBREF34   Our approach is naturally designed to train on word embeddings directly, thus we attempt to induce dependencies without using gold POS tags or other extra linguistic information 
Like previous work we use sections 02-21 of WSJ corpus as training data and evaluate on section 23, we remove punctuations and train the models on sentences of length INLINEFORM0 , “head-percolation” rules BIBREF39 are applied to obtain gold dependencies for evaluation  We train basic DMV, extended DMV (E-DMV) BIBREF35 and Gaussian DMV (which treats POS tag as unknown latent variables and generates observed word embeddings directly conditioned on them following Gaussian distribution) as baselines  Basic DMV and E-DMV are trained with Viterbi EM BIBREF40 on unsupervised POS tags induced from our Markov-structured model described in sec:pos  Multinomial parameters of the syntax model in both Gaussian DMV and our model are initialized with the pre-trained DMV baseline  Other parameters are initialized in the same way as in the POS tagging experiment  The directed dependency accuracy (DDA) is used for evaluation and we report accuracy on sentences of length INLINEFORM1 and all lengths  We train the parser until training data likelihood converges, and report the mean and standard deviation over 20 random restarts 
Our model directly observes word embeddings and does not require gold POS tags during training  Thus, results from related work trained on gold tags are not directly comparable  However, to measure how these systems might perform without gold tags, we run three recent state-of-the-art systems in our experimental setting: UR-A E-DMV BIBREF36 , Neural E-DMV BIBREF11 , and CRF Autoencoder (CRFAE) BIBREF37   We use unsupervised POS tags (induced from our Markov-structured model) in place of gold tags  We also train basic DMV on gold tags and include several state-of-the-art results on gold tags as reference points 
As shown in Table TABREF39 , our approach is able to improve over the Gaussian DMV by 4 8 points on length INLINEFORM0 and 4 8 points on all lengths, which suggests the additional latent embedding layer and neural projector are helpful  The proposed approach yields, to the best of our knowledge, state-of-the-art performance without gold POS annotation and without sentence-internal boundary information  DMV, UR-A E-DMV, Neural E-DMV, and CRFAE suffer a large decrease in performance when trained on unsupervised tags – an effect also seen in previous work BIBREF29 , BIBREF34   Since our approach induces latent POS tags jointly with dependency trees, it may be able to learn POS clusters that are more amenable to grammar induction than the unsupervised tags  We observe that CRFAE underperforms its gold-tag counterpart substantially  This may largely be a result of the model's reliance on prior linguistic rules that become unavailable when gold POS tag types are unknown  Many extensions to DMV can be considered orthogonal to our approach – they essentially focus on improving the syntax model  It is possible that incorporating these more sophisticated syntax models into our approach may lead to further improvements 
Sensitivity Analysis
In the above experiments we initialize the structured syntax components with the pre-trained Gaussian or discrete baseline, which is shown as a useful technique to help train our deep models  We further study the results with fully random initialization  In the POS tagging experiment, we report the results in Table TABREF48   While the performance with 4 layers is comparable to the pre-trained Gaussian initialization, deeper projections (8 or 16 layers) result in a dramatic drop in performance  This suggests that the structured syntax model with very deep projections is difficult to train from scratch, and a simpler projection might be a good compromise in the random initialization setting 
Different from the Markov prior in POS tagging experiments, our parsing model seems to be quite sensitive to the initialization  For example, directed accuracy of our approach on sentences of length INLINEFORM0 is below 40 0 with random initialization  This is consistent with previous work that has noted the importance of careful initialization for DMV-based models such as the commonly used harmonic initializer BIBREF2   However, it is not straightforward to apply the harmonic initializer for DMV directly in our model without using some kind of pre-training since we do not observe gold POS 
We investigate the effect of the choice of pre-trained embedding on performance while using our approach  To this end, we additionally include results using fastText embeddings BIBREF41 – which, in contrast with skip-gram embeddings, include character-level information  We set the context windows size to 1 and the dimension size to 100 as in the skip-gram training, while keeping other parameters set to their defaults  These results are summarized in Table TABREF50 and Table TABREF51   While fastText embeddings lead to reduced performance with our model, our approach still yields an improvement over the Gaussian baseline with the new observed embeddings space 
Qualitative Analysis of Embeddings
We perform qualitative analysis to understand how the latent embeddings help induce syntactic structures  First we filter out low-frequency words and punctuations in WSJ, and visualize the rest words (10k) with t-SNE BIBREF42 under different embeddings  We assign each word with its most likely gold POS tags in WSJ and color them according to the gold POS tags 
For our Markov-structured model, we have displayed the embedding space in Figure SECREF5 , where the gold POS clusters are well-formed  Further, we present five example target words and their five nearest neighbors in terms of cosine similarity  As shown in Table TABREF53 , the skip-gram embedding captures both semantic and syntactic aspects to some degree, yet our embeddings are able to focus especially on the syntactic aspects of words, in an unsupervised fashion without using any extra morphological information 
In Figure FIGREF54 we depict the learned latent embeddings with the DMV-structured syntax model  Unlike the Markov structure, the DMV structure maps a large subset of singular and plural nouns to the same overlapping region  However, two clusters of singular and plural nouns are actually separated  We inspect the two clusters and the overlapping region in Figure FIGREF54 , it turns out that the nouns in the separated clusters are words that can appear as subjects and, therefore, for which verb agreement is important to model  In contrast, the nouns in the overlapping region are typically objects  This demonstrates that the latent embeddings are focusing on aspects of language that are specifically important for modeling dependency without ever having seen examples of dependency parses  Some previous work has deliberately created embeddings to capture different notions of similarity BIBREF43 , BIBREF44 , while they use extra morphology or dependency annotations to guide the embedding learning, our approach provides a potential alternative to create new embeddings that are guided by structured syntax model, only using unlabeled text corpora 
Related Work
Our approach is related to flow-based generative models, which are first described in NICE BIBREF16 and have recently received more attention BIBREF17 , BIBREF19 , BIBREF18   This relevant work mostly adopts simple (e g  Gaussian) and fixed priors and does not attempt to learn interpretable latent structures  Another related generative model class is variational auto-encoders (VAEs) BIBREF45 that optimize a lower bound on the marginal data likelihood, and can be extended to learn latent structures BIBREF46 , BIBREF47   Against the flow-based models, VAEs remove the invertibility constraint but sacrifice the merits of exact inference and exact log likelihood computation, which potentially results in optimization challenges BIBREF48   Our approach can also be viewed in connection with generative adversarial networks (GANs) BIBREF49 that is a likelihood-free framework to learn implicit generative models  However, it is non-trivial for a gradient-based method like GANs to propagate gradients through discrete structures 
Conclusion
In this work, we define a novel generative approach to leverage continuous word representations for unsupervised learning of syntactic structure  Experiments on both POS induction and unsupervised dependency parsing tasks demonstrate the effectiveness of our proposed approach  Future work might explore more sophisticated invertible projections, or recurrent projections that jointly transform the entire input sequence 

ground_truth: No

No

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 do they evaluate on?
Context 4: 
Framework for Processing Portuguese Text ::: SVO Extraction
From the yield of the SRL (Semantic Role Labeling) module, our framework can distinguish actors, actions, places, time and objects from the sentences  Utilizing this extracted data, we can distinguish subject-verb-object (SVO) triples using the SVO extraction algorithm BIBREF14  The algorithm finds, for each sentence, the verb and the tuples related to that verb using Semantic Role Labeling (subsection SECREF8)  After the extraction of SVOs from texts, they are inserted into a specific event ontology (see section SECREF12 for the creation of a knowledge base) 
Framework for Processing Portuguese Text ::: Lexicon Matching
The sole purpose of this module is to find important terms and/or concepts from the extracted text  To do this, we use Euvovoc BIBREF6, a multilingual thesaurus that was developed for and by the European Union  The Euvovoc has 21 fields and each field is further divided into a variable number of micro-thesauri  Here, due to the application of this work in the Agatha project (mentioned in Section SECREF1), we use the terms of the criminal law BIBREF15 micro-thesaurus  Further, we classified each term of the criminal law micro-thesaurus into four categories namely, actor, event, place and object  The term classification can be seen in Table TABREF11 
After the classification of these terms, we implemented two different matching algorithms between the extracted words and the criminal law micro-thesaurus terms  The first is an exact string match wherein lowercase equivalents of the words of the input sentences are matched exactly with lower case equivalents of the predefined terms  The second matching algorithm uses Levenshtein distance BIBREF16, allowing some near-matches that are close enough to the target term 
Framework for Processing Portuguese Text ::: Linked Data: Ontology, Thesaurus and Terminology
In the computer science field, an ontology can be defined has:
a formal specification of a conceptualization;
shared vocabulary and taxonomy which models a domain with the definition of objects and/or concepts and their properties and relations;
the representation of entities, ideas, and events, along with their properties and relations, according to a system of categories 
A knowledge base is one kind of repository typically used to store answers to questions or solutions to problems enabling rapid search, retrieval, and reuse, either by an ontology or directly by those requesting support  For a more detailed description of ontologies and knowledge bases, see for instance BIBREF17 
For designing the ontology adequate for our goals, we referred to the Simple Event Model (SEM) BIBREF18 as a baseline model  A pictorial representation of this ontology is given in Figure FIGREF16
Considering the criminal law domain case study, we made a few changes to the original SEM ontology  The entities of the model are:
Actor: person involved with event
Place: location of the event
Time: time of the event
Object: that actor act upon
Organization: organization involved with event
Currency: money involved with event
The proposed ontology was designed in such a manner that it can incorporate information extracted from multiple documents  In this context, suppose that the source of documents is aare a legal police department, where each document isare under the hood of a particular case/crime; furthermoreFurther, a single case can have documents from multiple languages  Now, considering case 1 has 100 documents and case 2 has 100 documents then there is not only a connection among the documents of a single case but rather among all the cases with all the combined 200 documents  In this way, the proposed method is able to produce a detailed and well-connected knowledge base 
Figure FIGREF23 shows the proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents 
Protege BIBREF19 tool was used for creating the ontology and GraphDB BIBREF20 for populating & querying the data  GraphDB is an enterprise-ready Semantic Graph Database, compliant with W3C Standards  Semantic Graph Databases (also called RDF triplestores) provide the core infrastructure for solutions where modeling agility, data integration, relationship exploration, and cross-enterprise data publishing and consumption are important  GraphDB has a SPARQL (SQL-like query language) interface for RDF graph databases with the following types:
SELECT: returns tabular results
CONSTRUCT: creates a new RDF graph based on query results
ASK: returns "YES", if the query has a solution, otherwise "NO"
DESCRIBE: returns RDF data about a resource  This is useful when the RDF data structure in the data source is not known
INSERT: inserts triples into a graph
DELETE: deletes triples from a graph
Furthermore, we have extended the ontology BIBREF21 to connect the extracted terms with Eurovoc criminal law (discussed in subsection SECREF10) and IATE BIBREF7 terms  IATE (Interactive Terminology for Europe) is the EU's general terminology database and its aim is to provide a web-based infrastructure for all EU terminology resources, enhancing the availability and standardization of the information  The extended ontology has a number of sub-classes for Actor, Event, Object and Place classes detailed in Table TABREF30 
Discussion
We have defined a major design principle for our architecture: it should be modular and not rely on human made rules allowing, as much as possible, its independence from a specific language  In this way, its potential application to another language would be easier, simply by changing the modules or the models of specific modules  In fact, we have explored the use of already existing modules and adopted and integrated several of these tools into our pipeline 
It is important to point out that, as far as we know, there is no integrated architecture supporting the full processing pipeline for the Portuguese language  We evaluated several systems like Rembrandt BIBREF22 or LinguaKit: the former only has the initial steps of our proposal (until NER) and the later performed worse than our system 
This framework, developed within the context of the Agatha project (described in Section SECREF1) has the full processing pipeline for Portuguese texts: it receives sentences as input and outputs ontological information: a) first performs all NLP typical tasks until semantic role labelling; b) then, it extracts subject-verb-object triples; c) and, then, it performs ontology matching procedures  As a final result, the obtained output is inserted into a specialized ontology 
We are aware that each of the architecture modules can, and should, be improved but our main goal was the creation of a full working text processing pipeline for the Portuguese language 
Conclusions and Future Work
Besides the end–to–end NLP pipeline for the Portuguese language, the other main contributions of this work can be summarize as follows:
Development of an ontology for the criminal law domain;
Alignment of the Eurovoc thesaurus and IATE terminology with the ontology created;
Representation of the extracted events from texts in the linked knowledge base defined 
The obtained results support our claim that the proposed system can be used as a base tool for information extraction for the Portuguese language  Being composed by several modules, each of them with a high level of complexity, it is certain that our approach can be improved and an overall better performance can be achieved 
As future work we intend, not only to continue improving the individual modules, but also plan to extend this work to the:
automatic creation of event timelines;
incorporation in the knowledge base of information obtained from videos or pictures describing scenes relevant to criminal investigations 
Acknowledgments
The authors would like to thank COMPETE 2020, PORTUGAL 2020 Program, the European Union, and ALENTEJO 2020 for supporting this research as part of Agatha Project SI & IDT number 18022 (Intelligent analysis system of open of sources information for surveillance/crime control)  The authors would also like to thank LISP - Laboratory of Informatics, Systems and Parallelism 

ground_truth:  Wall Street Journal (WSJ) portion of the Penn Treebank

Unanswerable