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 features are used?
Context 1: Introduction
Cancer is one of the leading causes of death in the world, with over 80,000 deaths registered in Canada in 2017 (Canadian Cancer Statistics 2017)  A computer-aided system for cancer diagnosis usually involves a pathologist rendering a descriptive report after examining the tissue glass slides obtained from the biopsy of a patient  A pathology report contains specific analysis of cells and tissues, and other histopathological indicators that are crucial for diagnosing malignancies  An average sized laboratory may produces a large quantity of pathology reports annually (e g , in excess of 50,000), but these reports are written in mostly unstructured text and with no direct link to the tissue sample  Furthermore, the report for each patient is a personalized document and offers very high variability in terminology due to lack of standards and may even include misspellings and missing punctuation, clinical diagnoses interspersed with complex explanations, different terminology to label the same malignancy, and information about multiple carcinoma appearances included in a single report BIBREF0  
In Canada, each Provincial and Territorial Cancer Registry (PTCR) is responsible for collecting the data about cancer diseases and reporting them to Statistics Canada (StatCan)  Every year, Canadian Cancer Registry (CCR) uses the information sources of StatCan to compile an annual report on cancer and tumor diseases  Many countries have their own cancer registry programs  These programs rely on the acquisition of diagnostic, treatment, and outcome information through manual processing and interpretation from various unstructured sources (e g , pathology reports, autopsy/laboratory reports, medical billing summaries)  The manual classification of cancer pathology reports is a challenging, time-consuming task and requires extensive training BIBREF0  
With the continued growth in the number of cancer patients, and the increase in treatment complexity, cancer registries face a significant challenge in manually reviewing the large quantity of reports BIBREF1 , BIBREF0   In this situation, Natural Language Processing (NLP) systems can offer a unique opportunity to automatically encode the unstructured reports into structured data  Since, the registries already have access to the large quantity of historically labeled and encoded reports, a supervised machine learning approach of feature extraction and classification is a compelling direction for making their workflow more effective and streamlined  If successful, such a solution would enable processing reports in much lesser time allowing trained personnel to focus on their research and analysis  However, developing an automated solution with high accuracy and consistency across wide variety of reports is a challenging problem 
For cancer registries, an important piece of information in a pathology report is the associated ICD-O code which describes the patient's histological diagnosis, as described by the World Health Organization's (WHO) International Classification of Diseases for Oncology BIBREF2   Prediction of the primary diagnosis from a pathology report provides a valuable starting point for exploration of machine learning techniques for automated cancer surveillance  A major application for this purpose would be “auto-reporting” based on analysis of whole slide images, the digitization of the biopsy glass slides  Structured, summarized and categorized reports can be associated with the image content when searching in large archives  Such as system would be able to drastically increase the efficiency of diagnostic processes for the majority of cases where in spite of obvious primary diagnosis, still time and effort is required from the pathologists to write a descriptive report 
The primary objective of our study is to analyze the efficacy of existing machine learning approaches for the automated classification of pathology reports into different diagnosis categories  We demonstrate that TF-IDF feature vectors combined with linear SVM or XGBoost classifier can be an effective method for classification of the reports, achieving up to 83% accuracy  We also show that TF-IDF features are capable of identifying important keywords within a pathology report  Furthermore, we have created a new dataset consisting of 1,949 pathology reports across 37 primary diagnoses  Taken together, our exploratory experiments with a newly introduced dataset on pathology reports opens many new opportunities for researchers to develop a scalable and automatic information extraction from unstructured pathology reports 
Background
NLP approaches for information extraction within the biomedical research areas range from rule-based systems BIBREF3 , to domain-specific systems using feature-based classification BIBREF1 , to the recent deep networks for end-to-end feature extraction and classification BIBREF0   NLP has had varied degree of success with free-text pathology reports BIBREF4   Various studies have acknowledge the success of NLP in interpreting pathology reports, especially for classification tasks or extracting a single attribute from a report BIBREF4 , BIBREF5  
The Cancer Text Information Extraction System (caTIES) BIBREF6 is a framework developed in a caBIG project focuses on information extraction from pathology reports  Specifically, caTIES extracts information from surgical pathology reports (SPR) with good precision as well as recall 
Another system known as Open Registry BIBREF7 is capable of filtering the reports with disease codes containing cancer  In BIBREF8 , an approach called Automated Retrieval Console (ARC) is proposed which uses machine learning models to predict the degree of association of a given pathology or radiology with the cancer  The performance ranges from an F-measure of 0 75 for lung cancer to 0 94 for colorectal cancer  However, ARC uses domain-specific rules which hiders with the generalization of the approach to variety of pathology reports 
This research work is inspired by themes emerging in many of the above studies  Specifically, we are evaluating the task of predicting the primary diagnosis from the pathology report  Unlike previous approaches, the system does not rely on custom rule-based knowledge, domain specific features, balanced dataset with fewer number of classes 
Materials and Methods
We assembled a dataset of 1,949 cleaned pathology reports  Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes  The reports are collected from four different body parts or primary sites from multiple patients  The distribution of reports across different primary diagnoses and primary sites is reported in tab:report-distribution  The dataset was developed in three steps as follows 
Collecting pathology reports: The total of 11,112 pathology reports were downloaded from NCI's Genomic Data Commons (GDC) dataset in PDF format BIBREF9   Out of all PDF files, 1,949 reports were selected across multiple patients from four specific primary sites—thymus, testis, lung, and kidney  The selection was primarily made based on the quality of PDF files 
Cleaning reports: The next step was to extract the text content from these reports  Due to the significant time expense of manually re-typing all the pathology reports, we developed a new strategy to prepare our dataset  We applied an Optical Character Recognition (OCR) software to convert the PDF reports to text files  Then, we manually inspected all generated text files to fix any grammar/spelling issues and irrelevant characters as an artefact produced by the OCR system 
Splitting into training-testing data: We split the cleaned reports into 70% and 30% for training and testing, respectively  This split resulted in 1,364 training, and 585 testing reports 
Pre-Processing of Reports
We pre-processed the reports by setting their text content to lowercase and filtering out any non-alphanumeric characters  We used NLTK library to remove stopping words, e g , `the', `an', `was', `if' and so on BIBREF10   We then analyzed the reports to find common bigrams, such as “lung parenchyma”, “microscopic examination”, “lymph node” etc  We joined the biagrams with a hyphen, converting them into a single word  We further removed the words that occur less than 2% in each of the diagnostic category  As well, we removed the words that occur more than 90% across all the categories  We stored each pre-processed report in a separate text file 
TF-IDF features
TF-IDF stands for Term Frequency-Inverse Document Frequency, and it is a useful weighting scheme in information retrieval and text mining  TF-IDF signifies the importance of a term in a document within a corpus  It is important to note that a document here refers to a pathology report, a corpus refers to the collection of reports, and a term refers to a single word in a report  The TF-IDF weight for a term INLINEFORM0 in a document INLINEFORM1 is given by DISPLAYFORM0
We performed the following steps to transform a pathology report into a feature vector:
Create a set of vocabulary containing all unique words from all the pre-processed training reports 
Create a zero vector INLINEFORM0 of the same length as the vocabulary 
For each word INLINEFORM0 in a report INLINEFORM1 , set the corresponding index in INLINEFORM2 to INLINEFORM3  
The resultant INLINEFORM0 is a feature vector for the report INLINEFORM1 and it is a highly sparse vector 
Keyword extraction and topic modelling
The keyword extraction involves identifying important words within reports that summarizes its content  In contrast, the topic modelling allows grouping these keywords using an intelligent scheme, enabling users to further focus on certain aspects of a document  All the words in a pathology report are sorted according to their TF-IDF weights  The top INLINEFORM0 sorted words constitute the top INLINEFORM1 keywords for the report  The INLINEFORM2 is empirically set to 50 within this research  The extracted keywords are further grouped into different topics by using latent Dirichlet allocation (LDA) BIBREF11   The keywords in a report are highlighted using the color theme based on their topics 
Evaluation metrics
Each model is evaluated using two standard NLP metrics—micro and macro averaged F-scores, the harmonic mean of related metrics precision and recall  For each diagnostic category INLINEFORM0 from a set of 37 different classes INLINEFORM1 , the number of true positives INLINEFORM2 , false positives INLINEFORM3 , and false negatives INLINEFORM4 , the micro F-score is defined as DISPLAYFORM0
whereas macro F-score is given by DISPLAYFORM0
In summary, micro-averaged metrics have class representation roughly proportional to their test set representation (same as accuracy for classification problem with a single label per data point), whereas macro-averaged metrics are averaged by class without weighting by class prevalence BIBREF12  
Experimental setting
In this study, we performed two different series of experiments: i) evaluating the performance of TF-IDF features and various machine learning classifiers on the task of predicting primary diagnosis from the text content of a given report, and ii) using TF-IDF and LDA techniques to highlight the important keywords within a report  For the first experiment series, training reports are pre-processed, then their TF-IDF features are extracted  The TF-IDF features and the training labels are used to train different classification models  These different classification models and their hyper-parameters are reported in tab:classifier  The performance of classifiers is measured quantitatively on the test dataset using the evaluation metrics discussed in the previous section  For the second experiment series, a random report is selected and its top 50 keywords are extracted using TF-IDF weights  These 50 keywords are highlighted using different colors based on their associated topic, which are extracted through LDA  A non-expert based qualitative inspection is performed on the extracted keywords and their corresponding topics 
Experiment Series 1
A classification model is trained to predict the primary diagnosis given the content of the cancer pathology report  The performance results on this task are reported in tab:results  We can observe that the XGBoost classifier outperformed all other models for both the micro F-score metric, with a score of 0 92, and the macro F-score metric, with a score of 0 31  This was an improvement of 7% for the micro F-score over the next best model, SVM-L, and a marginal improvement of 5% for macro F-score  It is interesting to note that SVM with linear kernels performs much better than SVM with RBF kernel, scoring 9% on the macro F-score and 12% more on the micro F-score  It is suspected that since words used in primary diagnosis itself occur in some reports, thus enabling the linear models to outperform complex models 
Experiment Series 2
fig:keywords shows the top 50 keywords highlighted using TF-IDF and LDA  The proposed approach has performed well in highlighting the important regions, for example the topic highlighted with a red color containing “presence range tumor necrosis” provides useful biomarker information to readers 
Conclusions
We proposed a simple yet efficient TF-IDF method to extract and corroborate useful keywords from pathology cancer reports  Encoding a pathology report for cancer and tumor surveillance is a laborious task, and sometimes it is subjected to human errors and variability in the interpretation  One of the most important aspects of encoding a pathology report involves extracting the primary diagnosis  This may be very useful for content-based image retrieval to combine with visual information  We used existing classification model and TF-IDF features to predict the primary diagnosis  We achieved up to 92% accuracy using XGBoost classifier  The prediction accuracy empowers the adoption of machine learning methods for automated information extraction from pathology reports 
Context 2: 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 
Context 3: Introduction
With the increasing popularity of the Internet, online texts provided by social media platform (e g  Twitter) and news media sites (e g  Google news) have become important sources of real-world events  Therefore, it is crucial to automatically extract events from online texts 
Due to the high variety of events discussed online and the difficulty in obtaining annotated data for training, traditional template-based or supervised learning approaches for event extraction are no longer applicable in dealing with online texts  Nevertheless, newsworthy events are often discussed by many tweets or online news articles  Therefore, the same event could be mentioned by a high volume of redundant tweets or news articles  This property inspires the research community to devise clustering-based models BIBREF0 , BIBREF1 , BIBREF2 to discover new or previously unidentified events without extracting structured representations 
To extract structured representations of events such as who did what, when, where and why, Bayesian approaches have made some progress  Assuming that each document is assigned to a single event, which is modeled as a joint distribution over the named entities, the date and the location of the event, and the event-related keywords, Zhou et al  zhou2014simple proposed an unsupervised Latent Event Model (LEM) for open-domain event extraction  To address the limitation that LEM requires the number of events to be pre-set, Zhou et al  zhou2017event further proposed the Dirichlet Process Event Mixture Model (DPEMM) in which the number of events can be learned automatically from data  However, both LEM and DPEMM have two limitations: (1) they assume that all words in a document are generated from a single event which can be represented by a quadruple INLINEFORM0 entity, location, keyword, date INLINEFORM1   However, long texts such news articles often describe multiple events which clearly violates this assumption; (2) During the inference process of both approaches, the Gibbs sampler needs to compute the conditional posterior distribution and assigns an event for each document  This is time consuming and takes long time to converge 
To deal with these limitations, in this paper, we propose the Adversarial-neural Event Model (AEM) based on adversarial training for open-domain event extraction  The principle idea is to use a generator network to learn the projection function between the document-event distribution and four event related word distributions (entity distribution, location distribution, keyword distribution and date distribution)  Instead of providing an analytic approximation, AEM uses a discriminator network to discriminate between the reconstructed documents from latent events and the original input documents  This essentially helps the generator to construct a more realistic document from a random noise drawn from a Dirichlet distribution  Due to the flexibility of neural networks, the generator is capable of learning complicated nonlinear distributions  And the supervision signal provided by the discriminator will help generator to capture the event-related patterns  Furthermore, the discriminator also provides low-dimensional discriminative features which can be used to visualize documents and events 
The main contributions of the paper are summarized below:
Related Work
Our work is related to two lines of research, event extraction and Generative Adversarial Nets 
Event Extraction
Recently there has been much interest in event extraction from online texts, and approaches could be categorized as domain-specific and open-domain event extraction 
Domain-specific event extraction often focuses on the specific types of events (e g  sports events or city events)  Panem et al  panem2014structured devised a novel algorithm to extract attribute-value pairs and mapped them to manually generated schemes for extracting the natural disaster events  Similarly, to extract the city-traffic related event, Anantharam et al  anantharam2015extracting viewed the task as a sequential tagging problem and proposed an approach based on the conditional random fields  Zhang zhang2018event proposed an event extraction approach based on imitation learning, especially on inverse reinforcement learning 
Open-domain event extraction aims to extract events without limiting the specific types of events  To analyze individual messages and induce a canonical value for each event, Benson et al  benson2011event proposed an approach based on a structured graphical model  By representing an event with a binary tuple which is constituted by a named entity and a date, Ritter et al  ritter2012open employed some statistic to measure the strength of associations between a named entity and a date  The proposed system relies on a supervised labeler trained on annotated data  In BIBREF1 , Abdelhaq et al  developed a real-time event extraction system called EvenTweet, and each event is represented as a triple constituted by time, location and keywords  To extract more information, Wang el al  wang2015seeft developed a system employing the links in tweets and combing tweets with linked articles to identify events  Xia el al  xia2015new combined texts with the location information to detect the events with low spatial and temporal deviations  Zhou et al  zhou2014simple,zhou2017event represented event as a quadruple and proposed two Bayesian models to extract events from tweets 
Generative Adversarial Nets
As a neural-based generative model, Generative Adversarial Nets BIBREF3 have been extensively researched in natural language processing (NLP) community 
For text generation, the sequence generative adversarial network (SeqGAN) proposed in BIBREF4 incorporated a policy gradient strategy to optimize the generation process  Based on the policy gradient, Lin et al  lin2017adversarial proposed RankGAN to capture the rich structures of language by ranking and analyzing a collection of human-written and machine-written sentences  To overcome mode collapse when dealing with discrete data, Fedus et al  fedus2018maskgan proposed MaskGAN which used an actor-critic conditional GAN to fill in missing text conditioned on the surrounding context  Along this line, Wang et al  wang2018sentigan proposed SentiGAN to generate texts of different sentiment labels  Besides, Li et al  li2018learning improved the performance of semi-supervised text classification using adversarial training, BIBREF5 , BIBREF6 designed GAN-based models for distance supervision relation extraction 
Although various GAN based approaches have been explored for many applications, none of these approaches tackles open-domain event extraction from online texts  We propose a novel GAN-based event extraction model called AEM  Compared with the previous models, AEM has the following differences: (1) Unlike most GAN-based text generation approaches, a generator network is employed in AEM to learn the projection function between an event distribution and the event-related word distributions (entity, location, keyword, date)  The learned generator captures event-related patterns rather than generating text sequence; (2) Different from LEM and DPEMM, AEM uses a generator network to capture the event-related patterns and is able to mine events from different text sources (short and long)  Moreover, unlike traditional inference procedure, such as Gibbs sampling used in LEM and DPEMM, AEM could extract the events more efficiently due to the CUDA acceleration; (3) The discriminative features learned by the discriminator of AEM provide a straightforward way to visualize the extracted events 
Methodology
We describe Adversarial-neural Event Model (AEM) in this section  An event is represented as a quadruple < INLINEFORM0 >, where INLINEFORM1 stands for non-location named entities, INLINEFORM2 for a location, INLINEFORM3 for event-related keywords, INLINEFORM4 for a date, and each component in the tuple is represented by component-specific representative words 
AEM is constituted by three components: (1) The document representation module, as shown at the top of Figure FIGREF4 , defines a document representation approach which converts an input document from the online text corpus into INLINEFORM0 which captures the key event elements; (2) The generator INLINEFORM1 , as shown in the lower-left part of Figure FIGREF4 , generates a fake document INLINEFORM2 which is constituted by four multinomial distributions using an event distribution INLINEFORM3 drawn from a Dirichlet distribution as input; (3) The discriminator INLINEFORM4 , as shown in the lower-right part of Figure FIGREF4 , distinguishes the real documents from the fake ones and its output is subsequently employed as a learning signal to update the INLINEFORM5 and INLINEFORM6   The details of each component are presented below 
Document Representation
Each document INLINEFORM0 in a given corpus INLINEFORM1 is represented as a concatenation of 4 multinomial distributions which are entity distribution ( INLINEFORM2 ), location distribution ( INLINEFORM3 ), keyword distribution ( INLINEFORM4 ) and date distribution ( INLINEFORM5 ) of the document  As four distributions are calculated in a similar way, we only describe the computation of the entity distribution below as an example 
The entity distribution INLINEFORM0 is represented by a normalized INLINEFORM1 -dimensional vector weighted by TF-IDF, and it is calculated as: INLINEFORM2
where INLINEFORM0 is the pseudo corpus constructed by removing all non-entity words from INLINEFORM1 , INLINEFORM2 is the total number of distinct entities in a corpus, INLINEFORM3 denotes the number of INLINEFORM4 -th entity appeared in document INLINEFORM5 , INLINEFORM6 represents the number of documents in the corpus, and INLINEFORM7 is the number of documents that contain INLINEFORM8 -th entity, and the obtained INLINEFORM9 denotes the relevance between INLINEFORM10 -th entity and document INLINEFORM11  
Similarly, location distribution INLINEFORM0 , keyword distribution INLINEFORM1 and date distribution INLINEFORM2 of INLINEFORM3 could be calculated in the same way, and the dimensions of these distributions are denoted as INLINEFORM4 , INLINEFORM5 and INLINEFORM6 , respectively  Finally, each document INLINEFORM7 in the corpus is represented by a INLINEFORM8 -dimensional ( INLINEFORM9 = INLINEFORM10 + INLINEFORM11 + INLINEFORM12 + INLINEFORM13 ) vector INLINEFORM14 by concatenating four computed distributions 
Network Architecture
The generator network INLINEFORM0 is designed to learn the projection function between the document-event distribution INLINEFORM1 and the four document-level word distributions (entity distribution, location distribution, keyword distribution and date distribution) 
More concretely, INLINEFORM0 consists of a INLINEFORM1 -dimensional document-event distribution layer, INLINEFORM2 -dimensional hidden layer and INLINEFORM3 -dimensional event-related word distribution layer  Here, INLINEFORM4 denotes the event number, INLINEFORM5 is the number of units in the hidden layer, INLINEFORM6 is the vocabulary size and equals to INLINEFORM7 + INLINEFORM8 + INLINEFORM9 + INLINEFORM10   As shown in Figure FIGREF4 , INLINEFORM11 firstly employs a random document-event distribution INLINEFORM12 as an input  To model the multinomial property of the document-event distribution, INLINEFORM13 is drawn from a Dirichlet distribution parameterized with INLINEFORM14 which is formulated as: DISPLAYFORM0
where INLINEFORM0 is the hyper-parameter of the dirichlet distribution, INLINEFORM1 is the number of events which should be set in AEM, INLINEFORM2 , INLINEFORM3 represents the proportion of event INLINEFORM4 in the document and INLINEFORM5  
Subsequently, INLINEFORM0 transforms INLINEFORM1 into a INLINEFORM2 -dimensional hidden space using a linear layer followed by layer normalization, and the transformation is defined as: DISPLAYFORM0
where INLINEFORM0 represents the weight matrix of hidden layer, and INLINEFORM1 denotes the bias term, INLINEFORM2 is the parameter of LeakyReLU activation and is set to 0 1, INLINEFORM3 and INLINEFORM4 denote the normalized hidden states and the outputs of the hidden layer, and INLINEFORM5 represents the layer normalization 
Then, to project INLINEFORM0 into four document-level event related word distributions ( INLINEFORM1 , INLINEFORM2 , INLINEFORM3 and INLINEFORM4 shown in Figure FIGREF4 ), four subnets (each contains a linear layer, a batch normalization layer and a softmax layer) are employed in INLINEFORM5   And the exact transformation is based on the formulas below: DISPLAYFORM0
where INLINEFORM0 means softmax layer, INLINEFORM1 , INLINEFORM2 , INLINEFORM3 and INLINEFORM4 denote the weight matrices of the linear layers in subnets, INLINEFORM5 , INLINEFORM6 , INLINEFORM7 and INLINEFORM8 represent the corresponding bias terms, INLINEFORM9 , INLINEFORM10 , INLINEFORM11 and INLINEFORM12 are state vectors  INLINEFORM13 , INLINEFORM14 , INLINEFORM15 and INLINEFORM16 denote the generated entity distribution, location distribution, keyword distribution and date distribution, respectively, that correspond to the given event distribution INLINEFORM17   And each dimension represents the relevance between corresponding entity/location/keyword/date term and the input event distribution 
Finally, four generated distributions are concatenated to represent the generated document INLINEFORM0 corresponding to the input INLINEFORM1 : DISPLAYFORM0
The discriminator network INLINEFORM0 is designed as a fully-connected network which contains an input layer, a discriminative feature layer (discriminative features are employed for event visualization) and an output layer  In AEM, INLINEFORM1 uses fake document INLINEFORM2 and real document INLINEFORM3 as input and outputs the signal INLINEFORM4 to indicate the source of the input data (lower value denotes that INLINEFORM5 is prone to predict the input data as a fake document and vice versa) 
As have previously been discussed in BIBREF7 , BIBREF8 , lipschitz continuity of INLINEFORM0 network is crucial to the training of the GAN-based approaches  To ensure the lipschitz continuity of INLINEFORM1 , we employ the spectral normalization technique BIBREF9   More concretely, for each linear layer INLINEFORM2 (bias term is omitted for simplicity) in INLINEFORM3 , the weight matrix INLINEFORM4 is normalized by INLINEFORM5   Here, INLINEFORM6 is the spectral norm of the weight matrix INLINEFORM7 with the definition below: DISPLAYFORM0
which is equivalent to the largest singular value of INLINEFORM0   The weight matrix INLINEFORM1 is then normalized using: DISPLAYFORM0
Obviously, the normalized weight matrix INLINEFORM0 satisfies that INLINEFORM1 and further ensures the lipschitz continuity of the INLINEFORM2 network BIBREF9   To reduce the high cost of computing spectral norm INLINEFORM3 using singular value decomposition at each iteration, we follow BIBREF10 and employ the power iteration method to estimate INLINEFORM4 instead  With this substitution, the spectral norm can be estimated with very small additional computational time 
Objective and Training Procedure
The real document INLINEFORM0 and fake document INLINEFORM1 shown in Figure FIGREF4 could be viewed as random samples from two distributions INLINEFORM2 and INLINEFORM3 , and each of them is a joint distribution constituted by four Dirichlet distributions (corresponding to entity distribution, location distribution, keyword distribution and date distribution)  The training objective of AEM is to let the distribution INLINEFORM4 (produced by INLINEFORM5 network) to approximate the real data distribution INLINEFORM6 as much as possible 
To compare the different GAN losses, Kurach kurach2018gan takes a sober view of the current state of GAN and suggests that the Jansen-Shannon divergence used in BIBREF3 performs more stable than variant objectives  Besides, Kurach also advocates that the gradient penalty (GP) regularization devised in BIBREF8 will further improve the stability of the model  Thus, the objective function of the proposed AEM is defined as: DISPLAYFORM0
where INLINEFORM0 denotes the discriminator loss, INLINEFORM1 represents the gradient penalty regularization loss, INLINEFORM2 is the gradient penalty coefficient which trade-off the two components of objective, INLINEFORM3 could be obtained by sampling uniformly along a straight line between INLINEFORM4 and INLINEFORM5 , INLINEFORM6 denotes the corresponding distribution 
The training procedure of AEM is presented in Algorithm SECREF15 , where INLINEFORM0 is the event number, INLINEFORM1 denotes the number of discriminator iterations per generator iteration, INLINEFORM2 is the batch size, INLINEFORM3 represents the learning rate, INLINEFORM4 and INLINEFORM5 are hyper-parameters of Adam BIBREF11 , INLINEFORM6 denotes INLINEFORM7   In this paper, we set INLINEFORM8 , INLINEFORM9 , INLINEFORM10   Moreover, INLINEFORM11 , INLINEFORM12 and INLINEFORM13 are set as 0 0002, 0 5 and 0 999 
[!h] Training procedure for AEM [1] INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 the trained INLINEFORM7 and INLINEFORM8   Initial INLINEFORM9 parameters INLINEFORM10 and INLINEFORM11 parameter INLINEFORM12 INLINEFORM13 has not converged INLINEFORM14 INLINEFORM15 Sample INLINEFORM16 , Sample a random INLINEFORM17 Sample a random number INLINEFORM18 INLINEFORM19 INLINEFORM20 INLINEFORM21 INLINEFORM22 INLINEFORM23 INLINEFORM24 Sample INLINEFORM25 noise INLINEFORM26 INLINEFORM27
Event Generation
After the model training, the generator INLINEFORM0 learns the mapping function between the document-event distribution and the document-level event-related word distributions (entity, location, keyword and date)  In other words, with an event distribution INLINEFORM1 as input, INLINEFORM2 could generate the corresponding entity distribution, location distribution, keyword distribution and date distribution 
In AEM, we employ event seed INLINEFORM0 , an INLINEFORM1 -dimensional vector with one-hot encoding, to generate the event related word distributions  For example, in ten event setting, INLINEFORM2 represents the event seed of the first event  With the event seed INLINEFORM3 as input, the corresponding distributions could be generated by INLINEFORM4 based on the equation below: DISPLAYFORM0
where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 denote the entity distribution, location distribution, keyword distribution and date distribution of the first event respectively 
Experiments
In this section, we firstly describe the datasets and baseline approaches used in our experiments and then present the experimental results 
Experimental Setup
To validate the effectiveness of AEM for extracting events from social media (e g  Twitter) and news media sites (e g  Google news), three datasets (FSD BIBREF12 , Twitter, and Google datasets) are employed  Details are summarized below:
FSD dataset (social media) is the first story detection dataset containing 2,499 tweets  We filter out events mentioned in less than 15 tweets since events mentioned in very few tweets are less likely to be significant  The final dataset contains 2,453 tweets annotated with 20 events 
Twitter dataset (social media) is collected from tweets published in the month of December in 2010 using Twitter streaming API  It contains 1,000 tweets annotated with 20 events 
Google dataset (news article) is a subset of GDELT Event Database INLINEFORM0 , documents are retrieved by event related words  For example, documents which contain `malaysia', `airline', `search' and `plane' are retrieved for event MH370  By combining 30 events related documents, the dataset contains 11,909 news articles 
We choose the following three models as the baselines:
K-means is a well known data clustering algorithm, we implement the algorithm using sklearn toolbox, and represent documents using bag-of-words weighted by TF-IDF 
LEM BIBREF13 is a Bayesian modeling approach for open-domain event extraction  It treats an event as a latent variable and models the generation of an event as a joint distribution of its individual event elements  We implement the algorithm with the default configuration 
DPEMM BIBREF14 is a non-parametric mixture model for event extraction  It addresses the limitation of LEM that the number of events should be known beforehand  We implement the model with the default configuration 
For social media text corpus (FSD and Twitter), a named entity tagger specifically built for Twitter is used to extract named entities including locations from tweets  A Twitter Part-of-Speech (POS) tagger BIBREF15 is used for POS tagging and only words tagged with nouns, verbs and adjectives are retained as keywords  For the Google dataset, we use the Stanford Named Entity Recognizer to identify the named entities (organization, location and person)  Due to the `date' information not being provided in the Google dataset, we further divide the non-location named entities into two categories (`person' and `organization') and employ a quadruple <organization, location, person, keyword> to denote an event in news articles  We also remove common stopwords and only keep the recognized named entities and the tokens which are verbs, nouns or adjectives 
Experimental Results
To evaluate the performance of the proposed approach, we use the evaluation metrics such as precision, recall and F-measure  Precision is defined as the proportion of the correctly identified events out of the model generated events  Recall is defined as the proportion of correctly identified true events  For calculating the precision of the 4-tuple, we use following criteria:
(1) Do the entity/organization, location, date/person and keyword that we have extracted refer to the same event?
(2) If the extracted representation contains keywords, are they informative enough to tell us what happened?
Table TABREF35 shows the event extraction results on the three datasets  The statistics are obtained with the default parameter setting that INLINEFORM0 is set to 5, number of hidden units INLINEFORM1 is set to 200, and INLINEFORM2 contains three fully-connected layers  The event number INLINEFORM3 for three datasets are set to 25, 25 and 35, respectively  The examples of extracted events are shown in Table  TABREF36  
It can be observed that K-means performs the worst over all three datasets  On the social media datasets, AEM outpoerforms both LEM and DPEMM by 6 5% and 1 7% respectively in F-measure on the FSD dataset, and 4 4% and 3 7% in F-measure on the Twitter dataset  We can also observe that apart from K-means, all the approaches perform worse on the Twitter dataset compared to FSD, possibly due to the limited size of the Twitter dataset  Moreover, on the Google dataset, the proposed AEM performs significantly better than LEM and DPEMM  It improves upon LEM by 15 5% and upon DPEMM by more than 30% in F-measure  This is because: (1) the assumption made by LEM and DPEMM that all words in a document are generated from a single event is not suitable for long text such as news articles; (2) DPEMM generates too many irrelevant events which leads to a very low precision score  Overall, we see the superior performance of AEM across all datasets, with more significant improvement on the for Google datasets (long text) 
We next visualize the detected events based on the discriminative features learned by the trained INLINEFORM0 network in AEM  The t-SNE BIBREF16 visualization results on the datasets are shown in Figure FIGREF19   For clarity, each subplot is plotted on a subset of the dataset containing ten randomly selected events  It can be observed that documents describing the same event have been grouped into the same cluster 
To further evaluate if a variation of the parameters INLINEFORM0 (the number of discriminator iterations per generator iteration), INLINEFORM1 (the number of units in hidden layer) and the structure of generator INLINEFORM2 will impact the extraction performance, additional experiments have been conducted on the Google dataset, with INLINEFORM3 set to 5, 7 and 10, INLINEFORM4 set to 100, 150 and 200, and three INLINEFORM5 structures (3, 4 and 5 layers)  The comparison results on precision, recall and F-measure are shown in Figure FIGREF20   From the results, it could be observed that AEM with the 5-layer generator performs the best and achieves 96 7% in F-measure, and the worst F-measure obtained by AEM is 85 7%  Overall, the AEM outperforms all compared approaches acorss various parameter settings, showing relatively stable performance 
Finally, we compare in Figure FIGREF37 the training time required for each model, excluding the constant time required by each model to load the data  We could observe that K-means runs fastest among all four approaches  Both LEM and DPEMM need to sample the event allocation for each document and update the relevant counts during Gibbs sampling which are time consuming  AEM only requires a fraction of the training time compared to LEM and DPEMM  Moreover, on a larger dataset such as the Google dataset, AEM appears to be far more efficient compared to LEM and DPEMM 
Conclusions and Future Work
In this paper, we have proposed a novel approach based on adversarial training to extract the structured representation of events from online text  The experimental comparison with the state-of-the-art methods shows that AEM achieves improved extraction performance, especially on long text corpora with an improvement of 15% observed in F-measure  AEM only requires a fraction of training time compared to existing Bayesian graphical modeling approaches  In future work, we will explore incorporating external knowledge (e g  word relatedness contained in word embeddings) into the learning framework for event extraction  Besides, exploring nonparametric neural event extraction approaches and detecting the evolution of events over time from news articles are other promising future directions 
Acknowledgments
We would like to thank anonymous reviewers for their valuable comments and helpful suggestions  This work was funded by the National Key Research and Development Program of China (2016YFC1306704), the National Natural Science Foundation of China (61772132), the Natural Science Foundation of Jiangsu Province of China (BK20161430) 

ground_truth: Unanswerable

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

Please answer the questions based on the text provided below. What dataset did they use?
Context 4: Introduction
The recently introduced BERT model BIBREF0 exhibits strong performance on several language understanding benchmarks  To what extent does it capture syntax-sensitive structures?
Recent work examines the extent to which RNN-based models capture syntax-sensitive phenomena that are traditionally taken as evidence for the existence in hierarchical structure  In particular, in BIBREF1 we assess the ability of LSTMs to learn subject-verb agreement patterns in English, and evaluate on naturally occurring wikipedia sentences  BIBREF2 also consider subject-verb agreement, but in a “colorless green ideas” setting in which content words in naturally occurring sentences are replaced with random words with the same part-of-speech and inflection, thus ensuring a focus on syntax rather than on selectional-preferences based cues  BIBREF3 consider a wider range of syntactic phenomena (subject-verb agreement, reflexive anaphora, negative polarity items) using manually constructed stimuli, allowing for greater coverage and control than in the naturally occurring setting 
The BERT model is based on the “Transformer” architecture BIBREF4 , which—in contrast to RNNs—relies purely on attention mechanisms, and does not have an explicit notion of word order beyond marking each word with its absolute-position embedding  This reliance on attention may lead one to expect decreased performance on syntax-sensitive tasks compared to RNN (LSTM) models that do model word order directly, and explicitly track states across the sentence  Indeed, BIBREF5 finds that transformer-based models perform worse than LSTM models on the BIBREF1 agreement prediction dataset  In contrast, BIBREF6 find that self-attention performs on par with LSTM for syntax sensitive dependencies in the context of machine-translation, and performance on syntactic tasks is correlated with the number of attention heads in multi-head attention 
I adapt the evaluation protocol and stimuli of BIBREF1 , BIBREF2 and BIBREF3 to the bidirectional setting required by BERT, and evaluate the pre-trained BERT models (both the Large and the Base models)  Surprisingly (at least to me), the out-of-the-box models (without any task-specific fine-tuning) perform very well on all the syntactic tasks 
Methodology
I use the stimuli provided by BIBREF1 , BIBREF2 , BIBREF3 , but change the experimental protocol to adapt it to the bidirectional nature of the BERT model  This requires discarding some of the stimuli, as described below  Thus, the numbers are not strictly comparable to those reported in previous work 
Previous setups
All three previous work use uni-directional language-model-like models 
BIBREF1 start with existing sentences from wikipedia that contain a present-tense verb  They feed each sentence word by word into an LSTM, stop right before the focus verb, and ask the model to predict a binary plural/singular decision (supervised setup) or compare the probability assigned by a pre-trained language model (LM) to the plural vs singular forms of the verb (LM setup)  The evaluation is then performed on sentences with “agreement attractors” in which at there is at least one noun between the verb and its subject, and all of the nouns between the verb and subject are of the opposite number from the subject 
BIBREF2 also start with existing sentences  However, in order to control for the possibillity of the model learning to rely on “semantic” selectional-preferences cues rather than syntactic ones, they replace each content word with random words from the same part-of-speech and inflection  This results in “coloreless green ideas” nonce sentences  The evaluation is then performed similarly to the LM setup of BIBREF1 : the sentence is fed into a pre-traiend LSTM LM up to the focus verb, and the model is considered correct if the probability assigned to the correct inflection of the original verb form given the prefix is larger than that assigned to the incorrect inflection 
BIBREF3 focus on manually constructed and controlled stimuli, that also emphasizes linguistic structure over selectional preferences  They construct minimal pairs of grammatical and ungrammatical sentences, feed each one in its entirety into a pre-trained LSTM-LM, and compare the perplexity assigned by the model to the grammatical and ungrammatical sentences  The model is “correct” if it assigns the grammatical sentence a higher probability than to the ungrammatical one  Since the minimal pairs for most phenomena differ only in a single word (the focus verb), this scoring is very similar to the one used in the two previous works  However, it does consider the continuation of the sentence after the focus verb, and also allows for assessing phenomena that require change into two or more words (like negative polarity items) 
Adaptation to the BERT model
In contrast to these works, the BERT model is bi-directional: it is trained to predict the identity of masked words based on both the prefix and suffix surrounding these words  I adapt the uni-directional setup by feeding into BERT the complete sentence, while masking out the single focus verb  I then ask BERT for its word predictions for the masked position, and compare the score assigned to the original correct verb to the score assigned to the incorrect one

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. Do they report results only on English data?
Context 4: 
For example, for the sentence:
a 2002 systemic review of herbal products found that several herbs , including peppermint and caraway , have anti-dyspeptic effects for non-ulcer dyspepsia with “ encouraging safety profiles ”   (from BIBREF1 )
I feed into BERT:
[CLS] a 2002 systemic review of herbal products found that several herbs , including peppermint and caraway , [MASK] anti-dyspeptic effects for non-ulcer dyspepsia with “ encouraging safety profiles ”   and look for the score assigned to the words have and has at the masked position 
Similarly, for the pair
the game that the guard hates is bad  
the game that the guard hates are bad  
(from BIBREF3 ), I feed into BERT:
[CLS] the game that the guard hates [MASK] bad  
and compare the scores predicted for is and are 
This differs from BIBREF1 and BIBREF2 by considering the entire sentence (excluding the verb) and not just its prefix leading to the verb, and differs from BIBREF3 by conditioning the focus verb on bidirectional context 
I use the PyTorch implementation of BERT, with the pre-trained models supplied by Google  I experiment with the bert-large-uncased and bert-base-uncased models 
The bi-directional setup precludes using using the NPI stimuli of BIBREF3 , in which the minimal pair differs in two words position, which I discard from the evaluation  I also discard the agreement cases involving the verbs is or are in BIBREF1 and in BIBREF2 , because some of them are copular construction, in which strong agreement hints can be found also on the object following the verb  This is not an issue in the manually constructed BIBREF3 stimuli due to the patterns they chose 
Finally, I discard stimuli in which the focus verb or its plural/singular inflection does not appear as a single word in the BERT word-piece-based vocabulary (and hence cannot be predicted by the model)  This include discarding BIBREF3 stimuli involving the words swims or admires, resulting in 23,368 discarded pairs (out of 152,300)  I similarly discard 680 sentences from BIBREF1 where the focus verb or its inflection were one of 108 out-of-vocabulary tokens, and 28 sentence-pairs (8 tokens) from BIBREF2  
The BERT results are not directly comparable to the numbers reported in previous work  Beyond the differences due to bidirectionality and the discarded stimuli, the BERT models are also trained on a different and larger corpus (covering both wikipedia and books) 
Code is available at https://github com/yoavg/bert-syntax 
Results
Tables 1 , 2 and 3 show the results  All cases exhibit high scores—in the vast majority of the cases substantially higher than reported in previous work  As discussed above, the results are not directly comparable to previous work: the BERT models are trained on different (and larger) data, are allowed to access the suffix of the sentence in addition to its prefix, and are evaluated on somewhat different data due to discarding OOV items  Still, taken together, the high performance numbers indicate that the purely attention-based BERT models are likely capable of capturing the same kind of syntactic regularities that LSTM-based models are capable of capturing, at least as well as the LSTM models and probably better 
Another noticeable and interesting trend is that larger is not necessarily better: the BERT-Base model outperforms the BERT-Large model on many of the syntactic conditions 
Discussion
The BERT models perform remarkably well on all the syntactic test cases  I expected the attention-based mechanism to fail on these (compared to the LSTM-based models), and am surprised by these results  The BIBREF2 and BIBREF3 conditions rule out the possibility of overly relying on selectional preference cues or memorizing the wikipedia training data, and suggest real syntactic generalization is taking place  Exploring the extent to which deep purely-attention-based architectures such as BERT are capable of capturing hierarchy-sensitive and syntactic dependencies—as well as the mechanisms by which this is achieved—is a fascinating area for future research 

ground_truth: Unanswerable

Unanswerable