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. How many layers does the UTCNN model have?
Context 1: Introduction
This work is licenced under a Creative Commons Attribution 4 0 International License  License details: http://creativecommons org/licenses/by/4 0/ Deep neural networks have been widely used in text classification and have achieved promising results BIBREF0 , BIBREF1 , BIBREF2   Most focus on content information and use models such as convolutional neural networks (CNN) BIBREF3 or recursive neural networks BIBREF4   However, for user-generated posts on social media like Facebook or Twitter, there is more information that should not be ignored  On social media platforms, a user can act either as the author of a post or as a reader who expresses his or her comments about the post 
In this paper, we classify posts taking into account post authorship, likes, topics, and comments  In particular, users and their “likes” hold strong potential for text mining  For example, given a set of posts that are related to a specific topic, a user's likes and dislikes provide clues for stance labeling  From a user point of view, users with positive attitudes toward the issue leave positive comments on the posts with praise or even just the post's content; from a post point of view, positive posts attract users who hold positive stances  We also investigate the influence of topics: different topics are associated with different stance labeling tendencies and word usage  For example we discuss women's rights and unwanted babies on the topic of abortion, but we criticize medicine usage or crime when on the topic of marijuana BIBREF5   Even for posts on a specific topic like nuclear power, a variety of arguments are raised: green energy, radiation, air pollution, and so on  As for comments, we treat them as additional text information  The arguments in the comments and the commenters (the users who leave the comments) provide hints on the post's content and further facilitate stance classification 
In this paper, we propose the user-topic-comment neural network (UTCNN), a deep learning model that utilizes user, topic, and comment information  We attempt to learn user and topic representations which encode user interactions and topic influences to further enhance text classification, and we also incorporate comment information  We evaluate this model on a post stance classification task on forum-style social media platforms  The contributions of this paper are as follows: 1  We propose UTCNN, a neural network for text in modern social media channels as well as legacy social media, forums, and message boards — anywhere that reveals users, their tastes, as well as their replies to posts  2  When classifying social media post stances, we leverage users, including authors and likers  User embeddings can be generated even for users who have never posted anything  3  We incorporate a topic model to automatically assign topics to each post in a single topic dataset  4  We show that overall, the proposed method achieves the highest performance in all instances, and that all of the information extracted, whether users, topics, or comments, still has its contributions 
Extra-Linguistic Features for Stance Classification
In this paper we aim to use text as well as other features to see how they complement each other in a deep learning model  In the stance classification domain, previous work has showed that text features are limited, suggesting that adding extra-linguistic constraints could improve performance BIBREF6 , BIBREF7 , BIBREF8   For example, Hasan and Ng as well as Thomas et al  require that posts written by the same author have the same stance BIBREF9 , BIBREF10   The addition of this constraint yields accuracy improvements of 1–7% for some models and datasets  Hasan and Ng later added user-interaction constraints and ideology constraints BIBREF7 : the former models the relationship among posts in a sequence of replies and the latter models inter-topic relationships, e g , users who oppose abortion could be conservative and thus are likely to oppose gay rights 
For work focusing on online forum text, since posts are linked through user replies, sequential labeling methods have been used to model relationships between posts  For example, Hasan and Ng use hidden Markov models (HMMs) to model dependent relationships to the preceding post BIBREF9 ; Burfoot et al  use iterative classification to repeatedly generate new estimates based on the current state of knowledge BIBREF11 ; Sridhar et al  use probabilistic soft logic (PSL) to model reply links via collaborative filtering BIBREF12   In the Facebook dataset we study, we use comments instead of reply links  However, as the ultimate goal in this paper is predicting not comment stance but post stance, we treat comments as extra information for use in predicting post stance 
Deep Learning on Extra-Linguistic Features
In recent years neural network models have been applied to document sentiment classification BIBREF13 , BIBREF4 , BIBREF14 , BIBREF15 , BIBREF2   Text features can be used in deep networks to capture text semantics or sentiment  For example, Dong et al  use an adaptive layer in a recursive neural network for target-dependent Twitter sentiment analysis, where targets are topics such as windows 7 or taylor swift BIBREF16 , BIBREF17 ; recursive neural tensor networks (RNTNs) utilize sentence parse trees to capture sentence-level sentiment for movie reviews BIBREF4 ; Le and Mikolov predict sentiment by using paragraph vectors to model each paragraph as a continuous representation BIBREF18   They show that performance can thus be improved by more delicate text models 
Others have suggested using extra-linguistic features to improve the deep learning model  The user-word composition vector model (UWCVM) BIBREF19 is inspired by the possibility that the strength of sentiment words is user-specific; to capture this they add user embeddings in their model  In UPNN, a later extension, they further add a product-word composition as product embeddings, arguing that products can also show different tendencies of being rated or reviewed BIBREF20   Their addition of user information yielded 2–10% improvements in accuracy as compared to the above-mentioned RNTN and paragraph vector methods  We also seek to inject user information into the neural network model  In comparison to the research of Tang et al  on sentiment classification for product reviews, the difference is two-fold  First, we take into account multiple users (one author and potentially many likers) for one post, whereas only one user (the reviewer) is involved in a review  Second, we add comment information to provide more features for post stance classification  None of these two factors have been considered previously in a deep learning model for text stance classification  Therefore, we propose UTCNN, which generates and utilizes user embeddings for all users — even for those who have not authored any posts — and incorporates comments to further improve performance 
Method
In this section, we first describe CNN-based document composition, which captures user- and topic-dependent document-level semantic representation from word representations  Then we show how to add comment information to construct the user-topic-comment neural network (UTCNN) 
User- and Topic-dependent Document Composition
As shown in Figure FIGREF4 , we use a general CNN BIBREF3 and two semantic transformations for document composition   We are given a document with an engaged user INLINEFORM0 , a topic INLINEFORM1 , and its composite INLINEFORM2 words, each word INLINEFORM3 of which is associated with a word embedding INLINEFORM4 where INLINEFORM5 is the vector dimension  For each word embedding INLINEFORM6 , we apply two dot operations as shown in Equation EQREF6 : DISPLAYFORM0
where INLINEFORM0 models the user reading preference for certain semantics, and INLINEFORM1 models the topic semantics; INLINEFORM2 and INLINEFORM3 are the dimensions of transformed user and topic embeddings respectively  We use INLINEFORM4 to model semantically what each user prefers to read and/or write, and use INLINEFORM5 to model the semantics of each topic  The dot operation of INLINEFORM6 and INLINEFORM7 transforms the global representation INLINEFORM8 to a user-dependent representation  Likewise, the dot operation of INLINEFORM9 and INLINEFORM10 transforms INLINEFORM11 to a topic-dependent representation 
After the two dot operations on INLINEFORM0 , we have user-dependent and topic-dependent word vectors INLINEFORM1 and INLINEFORM2 , which are concatenated to form a user- and topic-dependent word vector INLINEFORM3   Then the transformed word embeddings INLINEFORM4 are used as the CNN input  Here we apply three convolutional layers on the concatenated transformed word embeddings INLINEFORM5 : DISPLAYFORM0
where INLINEFORM0 is the index of words; INLINEFORM1 is a non-linear activation function (we use INLINEFORM2 ); INLINEFORM5 is the convolutional filter with input length INLINEFORM6 and output length INLINEFORM7 , where INLINEFORM8 is the window size of the convolutional operation; and INLINEFORM9 and INLINEFORM10 are the output and bias of the convolution layer INLINEFORM11 , respectively  In our experiments, the three window sizes INLINEFORM12 in the three convolution layers are one, two, and three, encoding unigram, bigram, and trigram semantics accordingly 
After the convolutional layer, we add a maximum pooling layer among convolutional outputs to obtain the unigram, bigram, and trigram n-gram representations  This is succeeded by an average pooling layer for an element-wise average of the three maximized convolution outputs 
UTCNN Model Description
Figure FIGREF10 illustrates the UTCNN model  As more than one user may interact with a given post, we first add a maximum pooling layer after the user matrix embedding layer and user vector embedding layer to form a moderator matrix embedding INLINEFORM0 and a moderator vector embedding INLINEFORM1 for moderator INLINEFORM2 respectively, where INLINEFORM3 is used for the semantic transformation in the document composition process, as mentioned in the previous section  The term moderator here is to denote the pseudo user who provides the overall semantic/sentiment of all the engaged users for one document  The embedding INLINEFORM4 models the moderator stance preference, that is, the pattern of the revealed user stance: whether a user is willing to show his preference, whether a user likes to show impartiality with neutral statements and reasonable arguments, or just wants to show strong support for one stance  Ideally, the latent user stance is modeled by INLINEFORM5 for each user  Likewise, for topic information, a maximum pooling layer is added after the topic matrix embedding layer and topic vector embedding layer to form a joint topic matrix embedding INLINEFORM6 and a joint topic vector embedding INLINEFORM7 for topic INLINEFORM8 respectively, where INLINEFORM9 models the semantic transformation of topic INLINEFORM10 as in users and INLINEFORM11 models the topic stance tendency  The latent topic stance is also modeled by INLINEFORM12 for each topic 
As for comments, we view them as short documents with authors only but without likers nor their own comments  Therefore we apply document composition on comments although here users are commenters (users who comment)  It is noticed that the word embeddings INLINEFORM0 for the same word in the posts and comments are the same, but after being transformed to INLINEFORM1 in the document composition process shown in Figure FIGREF4 , they might become different because of their different engaged users  The output comment representation together with the commenter vector embedding INLINEFORM2 and topic vector embedding INLINEFORM3 are concatenated and a maximum pooling layer is added to select the most important feature for comments  Instead of requiring that the comment stance agree with the post, UTCNN simply extracts the most important features of the comment contents; they could be helpful, whether they show obvious agreement or disagreement  Therefore when combining comment information here, the maximum pooling layer is more appropriate than other pooling or merging layers  Indeed, we believe this is one reason for UTCNN's performance gains 
Finally, the pooled comment representation, together with user vector embedding INLINEFORM0 , topic vector embedding INLINEFORM1 , and document representation are fed to a fully connected network, and softmax is applied to yield the final stance label prediction for the post 
Experiment
We start with the experimental dataset and then describe the training process as well as the implementation of the baselines  We also implement several variations to reveal the effects of features: authors, likers, comment, and commenters  In the results section we compare our model with related work 
Dataset
We tested the proposed UTCNN on two different datasets: FBFans and CreateDebate  FBFans is a privately-owned, single-topic, Chinese, unbalanced, social media dataset, and CreateDebate is a public, multiple-topic, English, balanced, forum dataset  Results using these two datasets show the applicability and superiority for different topics, languages, data distributions, and platforms 
The FBFans dataset contains data from anti-nuclear-power Chinese Facebook fan groups from September 2013 to August 2014, including posts and their author and liker IDs  There are a total of 2,496 authors, 505,137 likers, 33,686 commenters, and 505,412 unique users  Two annotators were asked to take into account only the post content to label the stance of the posts in the whole dataset as supportive, neutral, or unsupportive (hereafter denoted as Sup, Neu, and Uns)  Sup/Uns posts were those in support of or against anti-reconstruction; Neu posts were those evincing a neutral standpoint on the topic, or were irrelevant  Raw agreement between annotators is 0 91, indicating high agreement  Specifically, Cohen’s Kappa for Neu and not Neu labeling is 0 58 (moderate), and for Sup or Uns labeling is 0 84 (almost perfect)  Posts with inconsistent labels were filtered out, and the development and testing sets were randomly selected from what was left  Posts in the development and testing sets involved at least one user who appeared in the training set  The number of posts for each stance is shown on the left-hand side of Table TABREF12   About twenty percent of the posts were labeled with a stance, and the number of supportive (Sup) posts was much larger than that of the unsupportive (Uns) ones: this is thus highly skewed data, which complicates stance classification  On average, 161 1 users were involved in one post  The maximum was 23,297 and the minimum was one (the author)  For comments, on average there were 3 comments per post  The maximum was 1,092 and the minimum was zero 
To test whether the assumption of this paper – posts attract users who hold the same stance to like them – is reliable, we examine the likes from authors of different stances  Posts in FBFans dataset are used for this analysis  We calculate the like statistics of each distinct author from these 32,595 posts  As the numbers of authors in the Sup, Neu and Uns stances are largely imbalanced, these numbers are normalized by the number of users of each stance  Table TABREF13 shows the results  Posts with stances (i e , not neutral) attract users of the same stance  Neutral posts also attract both supportive and neutral users, like what we observe in supportive posts, but just the neutral posts can attract even more neutral likers  These results do suggest that users prefer posts of the same stance, or at least posts of no obvious stance which might cause annoyance when reading, and hence support the user modeling in our approach 
The CreateDebate dataset was collected from an English online debate forum discussing four topics: abortion (ABO), gay rights (GAY), Obama (OBA), and marijuana (MAR)  The posts are annotated as for (F) and against (A)  Replies to posts in this dataset are also labeled with stance and hence use the same data format as posts  The labeling results are shown in the right-hand side of Table TABREF12   We observe that the dataset is more balanced than the FBFans dataset  In addition, there are 977 unique users in the dataset  To compare with Hasan and Ng's work, we conducted five-fold cross-validation and present the annotation results as the average number of all folds BIBREF9 , BIBREF5  
The FBFans dataset has more integrated functions than the CreateDebate dataset; thus our model can utilize all linguistic and extra-linguistic features  For the CreateDebate dataset, on the other hand, the like and comment features are not available (as there is a stance label for each reply, replies are evaluated as posts as other previous work) but we still implemented our model using the content, author, and topic information 
Settings
In the UTCNN training process, cross-entropy was used as the loss function and AdaGrad as the optimizer  For FBFans dataset, we learned the 50-dimensional word embeddings on the whole dataset using GloVe BIBREF21 to capture the word semantics; for CreateDebate dataset we used the publicly available English 50-dimensional word embeddings, pre-trained also using GloVe  These word embeddings were fixed in the training process  The learning rate was set to 0 03  All user and topic embeddings were randomly initialized in the range of [-0 1 0 1]  Matrix embeddings for users and topics were sized at 250 ( INLINEFORM0 ); vector embeddings for users and topics were set to length 10 
We applied the LDA topic model BIBREF22 on the FBFans dataset to determine the latent topics with which to build topic embeddings, as there is only one general known topic: nuclear power plants  We learned 100 latent topics and assigned the top three topics for each post  For the CreateDebate dataset, which itself constitutes four topics, the topic labels for posts were used directly without additionally applying LDA 
For the FBFans data we report class-based f-scores as well as the macro-average f-score ( INLINEFORM0 ) shown in equation EQREF19   DISPLAYFORM0
where INLINEFORM0 and INLINEFORM1 are the average precision and recall of the three class  We adopted the macro-average f-score as the evaluation metric for the overall performance because (1) the experimental dataset is severely imbalanced, which is common for contentious issues; and (2) for stance classification, content in minor-class posts is usually more important for further applications  For the CreateDebate dataset, accuracy was adopted as the evaluation metric to compare the results with related work BIBREF7 , BIBREF9 , BIBREF12  
Baselines
We pit our model against the following baselines: 1) SVM with unigram, bigram, and trigram features, which is a standard yet rather strong classifier for text features; 2) SVM with average word embedding, where a document is represented as a continuous representation by averaging the embeddings of the composite words; 3) SVM with average transformed word embeddings (the INLINEFORM0 in equation EQREF6 ), where a document is represented as a continuous representation by averaging the transformed embeddings of the composite words; 4) two mature deep learning models on text classification, CNN BIBREF3 and Recurrent Convolutional Neural Networks (RCNN) BIBREF0 , where the hyperparameters are based on their work; 5) the above SVM and deep learning models with comment information; 6) UTCNN without user information, representing a pure-text CNN model where we use the same user matrix and user embeddings INLINEFORM1 and INLINEFORM2 for each user; 7) UTCNN without the LDA model, representing how UTCNN works with a single-topic dataset; 8) UTCNN without comments, in which the model predicts the stance label given only user and topic information  All these models were trained on the training set, and parameters as well as the SVM kernel selections (linear or RBF) were fine-tuned on the development set  Also, we adopt oversampling on SVMs, CNN and RCNN because the FBFans dataset is highly imbalanced 
Results on FBFans Dataset
In Table TABREF22 we show the results of UTCNN and the baselines on the FBFans dataset  Here Majority yields good performance on Neu since FBFans is highly biased to the neutral class  The SVM models perform well on Sup and Neu but perform poorly for Uns, showing that content information in itself is insufficient to predict stance labels, especially for the minor class  With the transformed word embedding feature, SVM can achieve comparable performance as SVM with n-gram feature  However, the much fewer feature dimension of the transformed word embedding makes SVM with word embeddings a more efficient choice for modeling the large scale social media dataset  For the CNN and RCNN models, they perform slightly better than most of the SVM models but still, the content information is insufficient to achieve a good performance on the Uns posts  As to adding comment information to these models, since the commenters do not always hold the same stance as the author, simply adding comments and post contents together merely adds noise to the model 
Among all UTCNN variations, we find that user information is most important, followed by topic and comment information  UTCNN without user information shows results similar to SVMs — it does well for Sup and Neu but detects no Uns  Its best f-scores on both Sup and Neu among all methods show that with enough training data, content-based models can perform well; at the same time, the lack of user information results in too few clues for minor-class posts to either predict their stance directly or link them to other users and posts for improved performance  The 17 5% improvement when adding user information suggests that user information is especially useful when the dataset is highly imbalanced  All models that consider user information predict the minority class successfully  UCTNN without topic information works well but achieves lower performance than the full UTCNN model  The 4 9% performance gain brought by LDA shows that although it is satisfactory for single topic datasets, adding that latent topics still benefits performance: even when we are discussing the same topic, we use different arguments and supporting evidence  Lastly, we get 4 8% improvement when adding comment information and it achieves comparable performance to UTCNN without topic information, which shows that comments also benefit performance  For platforms where user IDs are pixelated or otherwise hidden, adding comments to a text model still improves performance  In its integration of user, content, and comment information, the full UTCNN produces the highest f-scores on all Sup, Neu, and Uns stances among models that predict the Uns class, and the highest macro-average f-score overall  This shows its ability to balance a biased dataset and supports our claim that UTCNN successfully bridges content and user, topic, and comment information for stance classification on social media text  Another merit of UTCNN is that it does not require a balanced training data  This is supported by its outperforming other models though no oversampling technique is applied to the UTCNN related experiments as shown in this paper  Thus we can conclude that the user information provides strong clues and it is still rich even in the minority class 
We also investigate the semantic difference when a user acts as an author/liker or a commenter  We evaluated a variation in which all embeddings from the same user were forced to be identical (this is the UTCNN shared user embedding setting in Table TABREF22 )  This setting yielded only a 2 5% improvement over the model without comments, which is not statistically significant  However, when separating authors/likers and commenters embeddings (i e , the UTCNN full model), we achieved much greater improvements (4 8%)  We attribute this result to the tendency of users to use different wording for different roles (for instance author vs commenter)  This is observed when the user, acting as an author, attempts to support her argument against nuclear power by using improvements in solar power; when acting as a commenter, though, she interacts with post contents by criticizing past politicians who supported nuclear power or by arguing that the proposed evacuation plan in case of a nuclear accident is ridiculous  Based on this finding, in the final UTCNN setting we train two user matrix embeddings for one user: one for the author/liker role and the other for the commenter role 
Results on CreateDebate Dataset
Table TABREF24 shows the results of UTCNN, baselines as we implemented on the FBFans datset and related work on the CreateDebate dataset  We do not adopt oversampling on these models because the CreateDebate dataset is almost balanced  In previous work, integer linear programming (ILP) or linear-chain conditional random fields (CRFs) were proposed to integrate text features, author, ideology, and user-interaction constraints, where text features are unigram, bigram, and POS-dependencies; the author constraint tends to require that posts from the same author for the same topic hold the same stance; the ideology constraint aims to capture inferences between topics for the same author; the user-interaction constraint models relationships among posts via user interactions such as replies BIBREF7 , BIBREF9  
The SVM with n-gram or average word embedding feature performs just similar to the majority  However, with the transformed word embedding, it achieves superior results  It shows that the learned user and topic embeddings really capture the user and topic semantics  This finding is not so obvious in the FBFans dataset and it might be due to the unfavorable data skewness for SVM  As for CNN and RCNN, they perform slightly better than most SVMs as we found in Table TABREF22 for FBFans 
Compared to the ILP BIBREF7 and CRF BIBREF9 methods, the UTCNN user embeddings encode author and user-interaction constraints, where the ideology constraint is modeled by the topic embeddings and text features are modeled by the CNN  The significant improvement achieved by UTCNN suggests the latent representations are more effective than overt model constraints 
The PSL model BIBREF12 jointly labels both author and post stance using probabilistic soft logic (PSL) BIBREF23 by considering text features and reply links between authors and posts as in Hasan and Ng's work  Table TABREF24 reports the result of their best AD setting, which represents the full joint stance/disagreement collective model on posts and is hence more relevant to UTCNN  In contrast to their model, the UTCNN user embeddings represent relationships between authors, but UTCNN models do not utilize link information between posts  Though the PSL model has the advantage of being able to jointly label the stances of authors and posts, its performance on posts is lower than the that for the ILP or CRF models  UTCNN significantly outperforms these models on posts and has the potential to predict user stances through the generated user embeddings 
For the CreateDebate dataset, we also evaluated performance when not using topic embeddings or user embeddings; as replies in this dataset are viewed as posts, the setting without comment embeddings is not available  Table TABREF24 shows the same findings as Table TABREF22 : the 21% improvement in accuracy demonstrates that user information is the most vital  This finding also supports the results in the related work: user constraints are useful and can yield 11 2% improvement in accuracy BIBREF7   Further considering topic information yields 3 4% improvement, suggesting that knowing the subject of debates provides useful information  In sum, Table TABREF22 together with Table TABREF24 show that UTCNN achieves promising performance regardless of topic, language, data distribution, and platform 
Conclusion
We have proposed UTCNN, a neural network model that incorporates user, topic, content and comment information for stance classification on social media texts  UTCNN learns user embeddings for all users with minimum active degree, i e , one post or one like  Topic information obtained from the topic model or the pre-defined labels further improves the UTCNN model  In addition, comment information provides additional clues for stance classification  We have shown that UTCNN achieves promising and balanced results  In the future we plan to explore the effectiveness of the UTCNN user embeddings for author stance classification 
Acknowledgements
Research of this paper was partially supported by Ministry of Science and Technology, Taiwan, under the contract MOST 104-2221-E-001-024-MY2 
Context 2: Introduction
Stance detection (also called stance identification or stance classification) is one of the considerably recent research topics in natural language processing (NLP)  It is usually defined as a classification problem where for a text and target pair, the stance of the author of the text for that target is expected as a classification output from the set: {Favor, Against, Neither} BIBREF0  
Stance detection is usually considered as a subtask of sentiment analysis (opinion mining) BIBREF1 topic in NLP  Both are mostly performed on social media texts, particularly on tweets, hence both are important components of social media analysis  Nevertheless, in sentiment analysis, the sentiment of the author of a piece of text usually as Positive, Negative, and Neutral is explored while in stance detection, the stance of the author of the text for a particular target (an entity, event, etc ) either explicitly or implicitly referred to in the text is considered  Like sentiment analysis, stance detection systems can be valuable components of information retrieval and other text analysis systems BIBREF0  
Previous work on stance detection include BIBREF2 where a stance classifier based on sentiment and arguing features is proposed in addition to an arguing lexicon automatically compiled  The ultimate approach performs better than distribution-based and uni-gram-based baseline systems BIBREF2   In BIBREF3 , the authors show that the use of dialogue structure improves stance detection in on-line debates  In BIBREF4 , Hasan and Ng carry out stance detection experiments using different machine learning algorithms, training data sets, features, and inter-post constraints in on-line debates, and draw insightful conclusions based on these experiments  For instance, they find that sequence models like HMMs perform better at stance detection when compared with non-sequence models like Naive Bayes (NB) BIBREF4   In another related study BIBREF5 , the authors conclude that topic-independent features can be exploited for disagreement detection in on-line dialogues  The employed features include agreement, cue words, denial, hedges, duration, polarity, and punctuation BIBREF5   Stance detection on a corpus of student essays is considered in BIBREF6   After using linguistically-motivated feature sets together with multivalued NB and SVM as the learning models, the authors conclude that they outperform two baseline approaches BIBREF6   In BIBREF7 , the author claims that Wikipedia can be used to determine stances about controversial topics based on their previous work regarding controversy extraction on the Web 
Among more recent related work, in BIBREF8 stance detection for unseen targets is studied and bidirectional conditional encoding is employed  The authors state that their approach achieves state-of-the art performance rates BIBREF8 on SemEval 2016 Twitter Stance Detection corpus BIBREF0   In BIBREF9 , a stance-community detection approach called SCIFNET is proposed  SCIFNET creates networks of people who are stance targets, automatically from the related document collections BIBREF9 using stance expansion and refinement techniques to arrive at stance-coherent networks  A tweet data set annotated with stance information regarding six predefined targets is proposed in BIBREF10 where this data set is annotated through crowdsourcing  The authors indicate that the data set is also annotated with sentiment information in addition to stance, so it can help reveal associations between stance and sentiment BIBREF10   Lastly, in BIBREF0 , SemEval 2016's aforementioned shared task on Twitter Stance Detection is described  Also provided are the results of the evaluations of 19 systems participating in two subtasks (one with training data set provided and the other without an annotated data set) of the shared task BIBREF0  
In this paper, we present a tweet data set in Turkish annotated with stance information, where the corresponding annotations are made publicly available  The domain of the tweets comprises two popular football clubs which constitute the targets of the tweets included  We also provide the evaluation results of SVM classifiers (for each target) on this data set using unigram, bigram, and hashtag features 
To the best of our knowledge, the current study is the first one to target at stance detection in Turkish tweets  Together with the provided annotated data set and the corresponding evaluations with the aforementioned SVM classifiers which can be used as baseline systems, our study will hopefully help increase social media analysis studies on Turkish content 
The rest of the paper is organized as follows: In Section SECREF2 , we describe our tweet data set annotated with the target and stance information  Section SECREF3 includes the details of our SVM-based stance classifiers and their evaluation results with discussions  Section SECREF4 includes future research topics based on the current study, and finally Section SECREF5 concludes the paper with a summary 
A Stance Detection Data Set
We have decided to consider tweets about popular sports clubs as our domain for stance detection  Considerable amounts of tweets are being published for sports-related events at every instant  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey  As is the case for the sentiment analysis tools, the outputs of the stance detection systems on a stream of tweets about these clubs can facilitate the use of the opinions of the football followers by these clubs 
In a previous study on the identification of public health-related tweets, two tweet data sets in Turkish (each set containing 1 million random tweets) have been compiled where these sets belong to two different periods of 20 consecutive days BIBREF11   We have decided to use one of these sets (corresponding to the period between August 18 and September 6, 2015) and firstly filtered the tweets using the possible names used to refer to the target clubs  Then, we have annotated the stance information in the tweets for these targets as Favor or Against  Within the course of this study, we have not considered those tweets in which the target is not explicitly mentioned, as our initial filtering process reveals 
For the purposes of the current study, we have not annotated any tweets with the Neither class  This stance class and even finer-grained classes can be considered in further annotation studies  We should also note that in a few tweets, the target of the stance was the management of the club while in some others a particular footballer of the club is praised or criticised  Still, we have considered the club as the target of the stance in all of the cases and carried out our annotations accordingly 
At the end of the annotation process, we have annotated 700 tweets, where 175 tweets are in favor of and 175 tweets are against Target-1, and similarly 175 tweets are in favor of and 175 are against Target-2  Hence, our data set is a balanced one although it is currently limited in size  The corresponding stance annotations are made publicly available at http://ceng

ground_truth: eight layers

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

Please answer the questions based on the text provided below. Which sports clubs are the targets?
Context 2: metu edu tr/ INLINEFORM0 e120329/ Turkish_Stance_Detection_Tweet_Dataset csv in Comma Separated Values (CSV) format  The file contains three columns with the corresponding headers  The first column is the tweet id of the corresponding tweet, the second column contains the name of the stance target, and the last column includes the stance of the tweet for the target as Favor or Against 
To the best of our knowledge, this is the first publicly-available stance-annotated data set for Turkish  Hence, it is a significant resource as there is a scarcity of annotated data sets, linguistic resources, and NLP tools available for Turkish  Additionally, to the best of our knowledge, it is also significant for being the first stance-annotated data set including sports-related tweets, as previous stance detection data sets mostly include on-line texts on political/ethical issues 
Stance Detection Experiments Using SVM Classifiers
It is emphasized in the related literature that unigram-based methods are reliable for the stance detection task BIBREF2 and similarly unigram-based models have been used as baseline models in studies such as BIBREF0   In order to be used as a baseline and reference system for further studies on stance detection in Turkish tweets, we have trained two SVM classifiers (one for each target) using unigrams as features  Before the extraction of unigrams, we have employed automated preprocessing to filter out the stopwords in our annotated data set of 700 tweets  The stopword list used is the list presented in BIBREF12 which, in turn, is the slightly extended version of the stopword list provided in BIBREF13  
We have used the SVM implementation available in the Weka data mining application BIBREF14 where this particular implementation employs the SMO algorithm BIBREF15 to train a classifier with a linear kernel  The 10-fold cross-validation results of the two classifiers are provided in Table TABREF1 using the metrics of precision, recall, and F-Measure 
The evaluation results are quite favorable for both targets and particularly higher for Target-1, considering the fact that they are the initial experiments on the data set  The performance of the classifiers is better for the Favor class for both targets when compared with the performance results for the Against class  This outcome may be due to the common use of some terms when expressing positive stance towards sports clubs in Turkish tweets  The same percentage of common terms may not have been observed in tweets during the expression of negative stances towards the targets  Yet, completely the opposite pattern is observed in stance detection results of baseline systems given in BIBREF0 , i e , better F-Measure rates have been obtained for the Against class when compared with the Favor class BIBREF0   Some of the baseline systems reported in BIBREF0 are SVM-based systems using unigrams and ngrams as features similar to our study, but their data sets include all three stance classes of Favor, Against, and Neither, while our data set comprises only tweets classified as belonging to Favor or Against classes  Another difference is that the data sets in BIBREF0 have been divided into training and test sets, while in our study we provide 10-fold cross-validation results on the whole data set  On the other hand, we should also note that SVM-based sentiment analysis systems (such as those given in BIBREF16 ) have been reported to achieve better F-Measure rates for the Positive sentiment class when compared with the results obtained for the Negative class  Therefore, our evaluation results for each stance class seem to be in line with such sentiment analysis systems  Yet, further experiments on the extended versions of our data set should be conducted and the results should again be compared with the stance detection results given in the literature 
We have also evaluated SVM classifiers which use only bigrams as features, as ngram-based classifiers have been reported to perform better for the stance detection problem BIBREF0   However, we have observed that using bigrams as the sole features of the SVM classifiers leads to quite poor results  This observation may be due to the relatively limited size of the tweet data set employed  Still, we can conclude that unigram-based features lead to superior results compared to the results obtained using bigrams as features, based on our experiments on our data set  Yet, ngram-based features may be employed on the extended versions of the data set to verify this conclusion within the course of future work 
With an intention to exploit the contribution of hashtag use to stance detection, we have also used the existence of hashtags in tweets as an additional feature to unigrams  The corresponding evaluation results of the SVM classifiers using unigrams together the existence of hashtags as features are provided in Table TABREF2  
When the results given in Table TABREF2 are compared with the results in Table TABREF1 , a slight decrease in F-Measure (0 5%) for Target-1 is observed, while the overall F-Measure value for Target-2 has increased by 1 8%  Although we could not derive sound conclusions mainly due to the relatively small size of our data set, the increase in the performance of the SVM classifier Target-2 is an encouraging evidence for the exploitation of hashtags in a stance detection system  We leave other ways of exploiting hashtags for stance detection as a future work 
To sum up, our evaluation results are significant as reference results to be used for comparison purposes and provides evidence for the utility of unigram-based and hashtag-related features in SVM classifiers for the stance detection problem in Turkish tweets 
Future Prospects
Future work based on the current study includes the following:
Conclusion
Stance detection is a considerably new research area in natural language processing and is considered within the scope of the well-studied topic of sentiment analysis  It is the detection of stance within text towards a target which may be explicitly specified in the text or not  In this study, we present a stance-annotated tweet data set in Turkish where the targets of the annotated stances are two popular sports clubs in Turkey  The corresponding annotations are made publicly-available for research purposes  To the best of our knowledge, this is the first stance detection data set for the Turkish language and also the first sports-related stance-annotated data set  Also presented in this study are SVM classifiers (one for each target) utilizing unigram and bigram features in addition to using the existence of hashtags as another feature  10-fold cross validation results of these classifiers are presented which can be used as reference results by prospective systems  Both the annotated data set and the classifiers with evaluations are significant since they are the initial contributions to stance detection problem in Turkish tweets 
Context 3: Introduction
Text simplification aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning, which can help children, non-native speakers, and people with cognitive disabilities, to understand text better  One of the methods of automatic text simplification can be generally divided into three categories: lexical simplification (LS) BIBREF0 , BIBREF1 , rule-based BIBREF2 , and machine translation (MT) BIBREF3 , BIBREF4   LS is mainly used to simplify text by substituting infrequent and difficult words with frequent and easier words  However, there are several challenges for the LS approach: a great number of transformation rules are required for reasonable coverage and should be applied based on the specific context; third, the syntax and semantic meaning of the sentence is hard to retain  Rule-based approaches use hand-crafted rules for lexical and syntactic simplification, for example, substituting difficult words in a predefined vocabulary  However, such approaches need a lot of human-involvement to manually define these rules, and it is impossible to give all possible simplification rules  MT-based approach has attracted great attention in the last several years, which addresses text simplification as a monolingual machine translation problem translating from 'ordinary' and 'simplified' sentences 
In recent years, neural Machine Translation (NMT) is a newly-proposed deep learning approach and achieves very impressive results BIBREF5 , BIBREF6 , BIBREF7   Unlike the traditional phrased-based machine translation system which operates on small components separately, NMT system is being trained end-to-end, without the need to have external decoders, language models or phrase tables  Therefore, the existing architectures in NMT are used for text simplification BIBREF8 , BIBREF4   However, most recent work using NMT is limited to the training data that are scarce and expensive to build  Language models trained on simplified corpora have played a central role in statistical text simplification BIBREF9 , BIBREF10   One main reason is the amount of available simplified corpora typically far exceeds the amount of parallel data  The performance of models can be typically improved when trained on more data  Therefore, we expect simplified corpora to be especially helpful for NMT models 
In contrast to previous work, which uses the existing NMT models, we explore strategy to include simplified training corpora in the training process without changing the neural network architecture  We first propose to pair simplified training sentences with synthetic ordinary sentences during training, and treat this synthetic data as additional training data  We obtain synthetic ordinary sentences through back-translation, i e  an automatic translation of the simplified sentence into the ordinary sentence BIBREF11   Then, we mix the synthetic data into the original (simplified-ordinary) data to train NMT model  Experimental results on two publicly available datasets show that we can improve the text simplification quality of NMT models by mixing simplified sentences into the training set over NMT model only using the original training data 
Related Work
Automatic TS is a complicated natural language processing (NLP) task, which consists of lexical and syntactic simplification levels BIBREF12   It has attracted much attention recently as it could make texts more accessible to wider audiences, and used as a pre-processing step, improve performances of various NLP tasks and systems BIBREF13 , BIBREF14 , BIBREF15   Usually, hand-crafted, supervised, and unsupervised methods based on resources like English Wikipedia and Simple English Wikipedia (EW-SEW) BIBREF10 are utilized for extracting simplification rules  It is very easy to mix up the automatic TS task and the automatic summarization task BIBREF3 , BIBREF16 , BIBREF6   TS is different from text summarization as the focus of text summarization is to reduce the length and redundant content 
At the lexical level, lexical simplification systems often substitute difficult words using more common words, which only require a large corpus of regular text to obtain word embeddings to get words similar to the complex word BIBREF1 , BIBREF9   Biran et al  BIBREF0 adopted an unsupervised method for learning pairs of complex and simpler synonyms from a corpus consisting of Wikipedia and Simple Wikipedia  At the sentence level, a sentence simplification model was proposed by tree transformation based on statistical machine translation (SMT) BIBREF3   Woodsend and Lapata BIBREF17 presented a data-driven model based on a quasi-synchronous grammar, a formalism that can naturally capture structural mismatches and complex rewrite operations  Wubben et al  BIBREF18 proposed a phrase-based machine translation (PBMT) model that is trained on ordinary-simplified sentence pairs  Xu et al  BIBREF19 proposed a syntax-based machine translation model using simplification-specific objective functions and features to encourage simpler output 
Compared with SMT, neural machine translation (NMT) has shown to produce state-of-the-art results BIBREF5 , BIBREF7   The central approach of NMT is an encoder-decoder architecture implemented by recurrent neural networks, which can represent the input sequence as a vector, and then decode that vector into an output sequence  Therefore, NMT models were used for text simplification task, and achieved good results BIBREF8 , BIBREF4 , BIBREF20   The main limitation of the aforementioned NMT models for text simplification depended on the parallel ordinary-simplified sentence pairs  Because ordinary-simplified sentence pairs are expensive and time-consuming to build, the available largest data is EW-SEW that only have 296,402 sentence pairs  The dataset is insufficiency for NMT model if we want to NMT model can obtain the best parameters  Considering simplified data plays an important role in boosting fluency for phrase-based text simplification, and we investigate the use of simplified data for text simplification  We are the first to show that we can effectively adapt neural translation models for text simplifiation with simplified corpora 
Simplified Corpora
We collected a simplified dataset from Simple English Wikipedia that are freely available, which has been previously used for many text simplification methods BIBREF0 , BIBREF10 , BIBREF3   The simple English Wikipedia is pretty easy to understand than normal English Wikipedia  We downloaded all articles from Simple English Wikipedia  For these articles, we removed stubs, navigation pages and any article that consisted of a single sentence  We then split them into sentences with the Stanford CorNLP BIBREF21 , and deleted these sentences whose number of words are smaller than 10 or large than 40  After removing repeated sentences, we chose 600K sentences as the simplified data with 11 6M words, and the size of vocabulary is 82K 
Text Simplification using Neural Machine Translation
Our work is built on attention-based NMT BIBREF5 as an encoder-decoder network with recurrent neural networks (RNN), which simultaneously conducts dynamic alignment and generation of the target simplified sentence 
The encoder uses a bidirectional RNN that consists of forward and backward RNN  Given a source sentence INLINEFORM0 , the forward RNN and backward RNN calculate forward hidden states INLINEFORM1 and backward hidden states INLINEFORM2 , respectively  The annotation vector INLINEFORM3 is obtained by concatenating INLINEFORM4 and INLINEFORM5  
The decoder is a RNN that predicts a target simplificated sentence with Gated Recurrent Unit (GRU) BIBREF22   Given the previously generated target (simplified) sentence INLINEFORM0 , the probability of next target word INLINEFORM1 is DISPLAYFORM0
where INLINEFORM0 is a non-linear function, INLINEFORM1 is the embedding of INLINEFORM2 , and INLINEFORM3 is a decoding state for time step INLINEFORM4  
State INLINEFORM0 is calculated by DISPLAYFORM0
where INLINEFORM0 is the activation function GRU 
The INLINEFORM0 is the context vector computed as a weighted annotation INLINEFORM1 , computed by DISPLAYFORM0
where the weight INLINEFORM0 is computed by DISPLAYFORM0 DISPLAYFORM1
where INLINEFORM0 , INLINEFORM1 and INLINEFORM2 are weight matrices  The training objective is to maximize the likelihood of the training data  Beam search is employed for decoding 
Synthetic Simplified Sentences
We train an auxiliary system using NMT model from the simplified sentence to the ordinary sentence, which is first trained on the available parallel data  For leveraging simplified sentences to improve the quality of NMT model for text simplification, we propose to adapt the back-translation approach proposed by Sennrich et al  BIBREF11 to our scenario  More concretely, Given one sentence in simplified sentences, we use the simplified-ordinary system in translate mode with greedy decoding to translate it to the ordinary sentences, which is denoted as back-translation  This way, we obtain a synthetic parallel simplified-ordinary sentences  Both the synthetic sentences and the available parallel data are used as training data for the original NMT system 
Evaluation
We evaluate the performance of text simplification using neural machine translation on available parallel sentences and additional simplified sentences 
Dataset  We use two simplification datasets (WikiSmall and WikiLarge)  WikiSmall consists of ordinary and simplified sentences from the ordinary and simple English Wikipedias, which has been used as benchmark for evaluating text simplification BIBREF17 , BIBREF18 , BIBREF8   The training set has 89,042 sentence pairs, and the test set has 100 pairs  WikiLarge is also from Wikipedia corpus whose training set contains 296,402 sentence pairs BIBREF19 , BIBREF20   WikiLarge includes 8 (reference) simplifications for 2,359 sentences split into 2,000 for development and 359 for testing 
Metrics  Three metrics in text simplification are chosen in this paper  BLEU BIBREF5 is one traditional machine translation metric to assess the degree to which translated simplifications differed from reference simplifications  FKGL measures the readability of the output BIBREF23   A small FKGL represents simpler output  SARI is a recent text-simplification metric by comparing the output against the source and reference simplifications BIBREF20  
We evaluate the output of all systems using human evaluation  The metric is denoted as Simplicity BIBREF8   The three non-native fluent English speakers are shown reference sentences and output sentences  They are asked whether the output sentence is much simpler (+2), somewhat simpler (+1), equally (0), somewhat more difficult (-1), and much more difficult (-2) than the reference sentence 
Methods  We use OpenNMT BIBREF24 as the implementation of the NMT system for all experiments BIBREF5   We generally follow the default settings and training procedure described by Klein et al (2017)  We replace out-of-vocabulary words with a special UNK symbol  At prediction time, we replace UNK words with the highest probability score from the attention layer  OpenNMT system used on parallel data is the baseline system  To obtain a synthetic parallel training set, we back-translate a random sample of 100K sentences from the collected simplified corpora  OpenNMT used on parallel data and synthetic data is our model  The benchmarks are run on a Intel(R) Core(TM) i7-5930K CPU@3 50GHz, 32GB Mem, trained on 1 GPU GeForce GTX 1080 (Pascal) with CUDA v  8 0 
We choose three statistical text simplification systems  PBMT-R is a phrase-based method with a reranking post-processing step BIBREF18   Hybrid performs sentence splitting and deletion operations based on discourse representation structures, and then simplifies sentences with PBMT-R BIBREF25   SBMT-SARI BIBREF19 is syntax-based translation model using PPDB paraphrase database BIBREF26 and modifies tuning function (using SARI)  We choose two neural text simplification systems  NMT is a basic attention-based encoder-decoder model which uses OpenNMT framework to train with two LSTM layers, hidden states of size 500 and 500 hidden units, SGD optimizer, and a dropout rate of 0 3 BIBREF8   Dress is an encoder-decoder model coupled with a deep reinforcement learning framework, and the parameters are chosen according to the original paper BIBREF20   For the experiments with synthetic parallel data, we back-translate a random sample of 60 000 sentences from the collected simplified sentences into ordinary sentences  Our model is trained on synthetic data and the available parallel data, denoted as NMT+synthetic 
Results  Table 1 shows the results of all models on WikiLarge dataset  We can see that our method (NMT+synthetic) can obtain higher BLEU, lower FKGL and high SARI compared with other models, except Dress on FKGL and SBMT-SARI on SARI  It verified that including synthetic data during training is very effective, and yields an improvement over our baseline NMF by 2 11 BLEU, 1 7 FKGL and 1 07 SARI  We also substantially outperform Dress, who previously reported SOTA result  The results of our human evaluation using Simplicity are also presented in Table 1  NMT on synthetic data is significantly better than PBMT-R, Dress, and SBMT-SARI on Simplicity  It indicates that our method with simplified data is effective at creating simpler output 
Results on WikiSmall dataset are shown in Table 2  We see substantial improvements (6 37 BLEU) than NMT from adding simplified training data with synthetic ordinary sentences  Compared with statistical machine translation models (PBMT-R, Hybrid, SBMT-SARI), our method (NMT+synthetic) still have better results, but slightly worse FKGL and SARI  Similar to the results in WikiLarge, the results of our human evaluation using Simplicity outperforms the other models  In conclusion, Our method produces better results comparing with the baselines, which demonstrates the effectiveness of adding simplified training data 
Conclusion
In this paper, we propose one simple method to use simplified corpora during training of NMT systems, with no changes to the network architecture  In the experiments on two datasets, we achieve substantial gains in all tasks, and new SOTA results, via back-translation of simplified sentences into the ordinary sentences, and treating this synthetic data as additional training data  Because we do not change the neural network architecture to integrate simplified corpora, our method can be easily applied to other Neural Text Simplification (NTS) systems  We expect that the effectiveness of our method not only varies with the quality of the NTS system used for back-translation, but also depends on the amount of available parallel and simplified corpora  In the paper, we have only utilized data from Wikipedia for simplified sentences  In the future, many other text sources are available and the impact of not only size, but also of domain should be investigated 

ground_truth: Galatasaray, Fenerbahçe

Galatasaray , Fenerbahçe 

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

Please answer the questions based on the text provided below. what are the sizes of both datasets?
Context 4:  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: training set has 89,042 sentence pairs, and the test set has 100 pairs, training set contains 296,402, 2,000 for development and 359 for testing

WikiSmall  89 142 sentence pair and  WikiLarge 298 761 sentence pairs.