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 were the baselines?
Context 1: Introduction
Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words  Hybrid systems combine hidden Markov models to model state dependencies with neural networks to predict states BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3   Newer approaches such as end-to-end (E2E) systems reduce the overall complexity of the final system 
Our research builds on prior work that has explored using time-delay neural networks (TDNN), other forms of convolutional neural networks, and Connectionist Temporal Classification (CTC) loss BIBREF4 , BIBREF5 , BIBREF6   We took inspiration from wav2letter BIBREF6 , which uses 1D-convolution layers  Liptchinsky et al  BIBREF7 improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) BIBREF8 , weight normalization BIBREF9 and dropout 
By building a deeper and larger capacity network, we aim to demonstrate that we can match or outperform non end-to-end models on the LibriSpeech and 2000hr Fisher+Switchboard tasks  Like wav2letter, our architecture, Jasper, uses a stack of 1D-convolution layers, but with ReLU and batch normalization BIBREF10   We find that ReLU and batch normalization outperform other activation and normalization schemes that we tested for convolutional ASR  As a result, Jasper's architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs 
It is possible to increase the capacity of the Jasper model by stacking these operations  Our largest version uses 54 convolutional layers (333M parameters), while our small model uses 34 (201M parameters)  We use residual connections to enable this level of depth  We investigate a number of residual options and propose a new residual connection topology we call Dense Residual (DR) 
Integrating our best acoustic model with a Transformer-XL BIBREF11 language model allows us to obtain new state-of-the-art (SOTA) results on LibriSpeech BIBREF12 test-clean of 2 95% WER and SOTA results among end-to-end models on LibriSpeech test-other  We show competitive results on Wall Street Journal (WSJ), and 2000hr Fisher+Switchboard (F+S)  Using only greedy decoding without a language model we achieve 3 86% WER on LibriSpeech test-clean 
This paper makes the following contributions:
Jasper Architecture
Jasper is a family of end-to-end ASR models that replace acoustic and pronunciation models with a convolutional neural network  Jasper uses mel-filterbank features calculated from 20ms windows with a 10ms overlap, and outputs a probability distribution over characters per frame  Jasper has a block architecture: a Jasper INLINEFORM0 x INLINEFORM1 model has INLINEFORM2 blocks, each with INLINEFORM3 sub-blocks  Each sub-block applies the following operations: a 1D-convolution, batch norm, ReLU, and dropout  All sub-blocks in a block have the same number of output channels 
Each block input is connected directly into the last sub-block via a residual connection  The residual connection is first projected through a 1x1 convolution to account for different numbers of input and output channels, then through a batch norm layer  The output of this batch norm layer is added to the output of the batch norm layer in the last sub-block  The result of this sum is passed through the activation function and dropout to produce the output of the sub-block 
The sub-block architecture of Jasper was designed to facilitate fast GPU inference  Each sub-block can be fused into a single GPU kernel: dropout is not used at inference-time and is eliminated, batch norm can be fused with the preceding convolution, ReLU clamps the result, and residual summation can be treated as a modified bias term in this fused operation 
All Jasper models have four additional convolutional blocks: one pre-processing and three post-processing  See Figure FIGREF7 and Table TABREF8 for details 
We also build a variant of Jasper, Jasper Dense Residual (DR)  Jasper DR follows DenseNet BIBREF15 and DenseRNet BIBREF16 , but instead of having dense connections within a block, the output of a convolution block is added to the inputs of all the following blocks  While DenseNet and DenseRNet concatenates the outputs of different layers, Jasper DR adds them in the same way that residuals are added in ResNet  As explained below, we find addition to be as effective as concatenation 
Normalization and Activation
In our study, we evaluate performance of models with:
3 types of normalization: batch norm BIBREF10 , weight norm BIBREF9 , and layer norm BIBREF17
3 types of rectified linear units: ReLU, clipped ReLU (cReLU), and leaky ReLU (lReLU)
2 types of gated units: gated linear units (GLU) BIBREF8 , and gated activation units (GAU) BIBREF18
All experiment results are shown in Table TABREF15   We first experimented with a smaller Jasper5x3 model to pick the top 3 settings before training on larger Jasper models  We found that layer norm with GAU performed the best on the smaller model  Layer norm with ReLU and batch norm with ReLU came second and third in our tests  Using these 3, we conducted further experiments on a larger Jasper10x4  For larger models, we noticed that batch norm with ReLU outperformed other choices  Thus, leading us to decide on batch normalization and ReLU for our architecture 
During batching, all sequences are padded to match the longest sequence  These padded values caused issues when using layer norm  We applied a sequence mask to exclude padding values from the mean and variance calculation  Further, we computed mean and variance over both the time dimension and channels similar to the sequence-wise normalization proposed by Laurent et al  BIBREF19   In addition to masking layer norm, we additionally applied masking prior to the convolution operation, and masking the mean and variance calculation in batch norm  These results are shown in Table TABREF16   Interestingly, we found that while masking before convolution gives a lower WER, using masks for both convolutions and batch norm results in worse performance 
As a final note, we found that training with weight norm was very unstable leading to exploding activations 
Residual Connections
For models deeper than Jasper 5x3, we observe consistently that residual connections are necessary for training to converge  In addition to the simple residual and dense residual model described above, we investigated DenseNet BIBREF15 and DenseRNet BIBREF16 variants of Jasper  Both connect the outputs of each sub-block to the inputs of following sub-blocks within a block  DenseRNet, similar to Dense Residual, connects the output of each output of each block to the input of all following blocks  DenseNet and DenseRNet combine residual connections using concatenation whereas Residual and Dense Residual use addition  We found that Dense Residual and DenseRNet perform similarly with each performing better on specific subsets of LibriSpeech  We decided to use Dense Residual for subsequent experiments  The main reason is that due to concatenation, the growth factor for DenseNet and DenseRNet requires tuning for deeper models whereas Dense Residual simply just repeats a sub-blocks 
Language Model
A language model (LM) is a probability distribution over arbitrary symbol sequences INLINEFORM0 such that more likely sequences are assigned high probabilities  LMs are frequently used to condition beam search  During decoding, candidates are evaluated using both acoustic scores and LM scores  Traditional N-gram LMs have been augmented with neural LMs in recent work BIBREF20 , BIBREF21 , BIBREF22  
We experiment with statistical N-gram language models BIBREF23 and neural Transformer-XL BIBREF11 models  Our best results use acoustic and word-level N-gram language models to generate a candidate list using beam search with a width of 2048  Next, an external Transformer-XL LM rescores the final list  All LMs were trained on datasets independently from acoustic models  We show results with the neural LM in our Results section  We observed a strong correlation between the quality of the neural LM (measured by perplexity) and WER as shown in Figure FIGREF20  
NovoGrad
For training, we use either Stochastic Gradient Descent (SGD) with momentum or our own NovoGrad, an optimizer similar to Adam BIBREF14 , except that its second moments are computed per layer instead of per weight  Compared to Adam, it reduces memory consumption and we find it to be more numerically stable 
At each step INLINEFORM0 , NovoGrad computes the stochastic gradient INLINEFORM1 following the regular forward-backward pass  Then the second-order moment INLINEFORM2 is computed for each layer INLINEFORM3 similar to ND-Adam BIBREF27 : DISPLAYFORM0
The second-order moment INLINEFORM0 is used to re-scale gradients INLINEFORM1 before calculating the first-order moment INLINEFORM2 : DISPLAYFORM0
If L2-regularization is used, a weight decay INLINEFORM0 is added to the re-scaled gradient (as in AdamW BIBREF28 ): DISPLAYFORM0
Finally, new weights are computed using the learning rate INLINEFORM0 : DISPLAYFORM0
Using NovoGrad instead of SGD with momentum, we decreased the WER on dev-clean LibriSpeech from 4 00% to 3 64%, a relative improvement of 9% for Jasper DR 10x5  We will further analyze NovoGrad in forthcoming work 
Results
We evaluate Jasper across a number of datasets in various domains  In all experiments, we use dropout and weight decay as regularization  At training time, we use speed perturbation with fixed +/-10% BIBREF29 for LibriSpeech  For WSJ and Hub5'00, we use a random speed perturbation factor between [-10%, 10%] as each utterance is fed into the model  All models have been trained on NVIDIA DGX-1 in mixed precision BIBREF30 using OpenSeq2Seq BIBREF31   Source code, training configurations, and pretrained models are available 
Read Speech
We evaluated the performance of Jasper on two read speech datasets: LibriSpeech and Wall Street Journal (WSJ)  For LibriSpeech, we trained Jasper DR 10x5 using our NovoGrad optimizer for 400 epochs  We achieve SOTA performance on the test-clean subset and SOTA among end-to-end speech recognition models on test-other 
We trained a smaller Jasper 10x3 model with SGD with momentum optimizer for 400 epochs on a combined WSJ dataset (80 hours): LDC93S6A (WSJ0) and LDC94S13A (WSJ1)  The results are provided in Table TABREF29  
Conversational Speech
We also evaluate the Jasper model's performance on a conversational English corpus  The Hub5 Year 2000 (Hub5'00) evaluation (LDC2002S09, LDC2005S13) is widely used in academia  It is divided into two subsets: Switchboard (SWB) and Callhome (CHM)  The training data for both the acoustic and language models consisted of the 2000hr Fisher+Switchboard training data (LDC2004S13, LDC2005S13, LDC97S62)  Jasper DR 10x5 was trained using SGD with momentum for 50 epochs  We compare to other models trained using the same data and report Hub5'00 results in Table TABREF31  
We obtain good results for SWB  However, there is work to be done to improve WER on harder tasks such as CHM 
Conclusions
We have presented a new family of neural architectures for end-to-end speech recognition  Inspired by wav2letter's convolutional approach, we build a deep and scalable model, which requires a well-designed residual topology, effective regularization, and a strong optimizer  As our architecture studies demonstrated, a combination of standard components leads to SOTA results on LibriSpeech and competitive results on other benchmarks  Our Jasper architecture is highly efficient for training and inference, and serves as a good baseline approach on top of which to explore more sophisticated regularization, data augmentation, loss functions, language models, and optimization strategies  We are interested to see if our approach can continue to scale to deeper models and larger datasets 
Context 2: 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 3: Introduction
This work is licenced under a Creative Commons Attribution 4 0 International Licence  Licence details: http://creativecommons org/licenses/by/4 0/
In the spirit of the brevity of social media's messages and reactions, people have got used to express feelings minimally and symbolically, as with hashtags on Twitter and Instagram  On Facebook, people tend to be more wordy, but posts normally receive more simple “likes” than longer comments  Since February 2016, Facebook users can express specific emotions in response to a post thanks to the newly introduced reaction feature (see Section SECREF2 ), so that now a post can be wordlessly marked with an expression of say “joy" or “surprise" rather than a generic “like” 
It has been observed that this new feature helps Facebook to know much more about their users and exploit this information for targeted advertising BIBREF0 , but interest in people's opinions and how they feel isn't limited to commercial reasons, as it invests social monitoring, too, including health care and education BIBREF1   However, emotions and opinions are not always expressed this explicitly, so that there is high interest in developing systems towards their automatic detection  Creating manually annotated datasets large enough to train supervised models is not only costly, but also—especially in the case of opinions and emotions—difficult, due to the intrinsic subjectivity of the task BIBREF2 , BIBREF3   Therefore, research has focused on unsupervised methods enriched with information derived from lexica, which are manually created BIBREF3 , BIBREF4   Since go2009twitter have shown that happy and sad emoticons can be successfully used as signals for sentiment labels, distant supervision, i e  using some reasonably safe signals as proxies for automatically labelling training data BIBREF5 , has been used also for emotion recognition, for example exploiting both emoticons and Twitter hashtags BIBREF6 , but mainly towards creating emotion lexica  mohammad2015using use hashtags, experimenting also with highly fine-grained emotion sets (up to almost 600 emotion labels), to create the large Hashtag Emotion Lexicon  Emoticons are used as proxies also by hallsmarmulti, who use distributed vector representations to find which words are interchangeable with emoticons but also which emoticons are used in a similar context 
We take advantage of distant supervision by using Facebook reactions as proxies for emotion labels, which to the best of our knowledge hasn't been done yet, and we train a set of Support Vector Machine models for emotion recognition  Our models, differently from existing ones, exploit information which is acquired entirely automatically, and achieve competitive or even state-of-the-art results for some of the emotion labels on existing, standard evaluation datasets  For explanatory purposes, related work is discussed further and more in detail when we describe the benchmarks for evaluation (Section SECREF3 ) and when we compare our models to existing ones (Section SECREF5 )  We also explore and discuss how choosing different sets of Facebook pages as training data provides an intrinsic domain-adaptation method 
Facebook reactions as labels
For years, on Facebook people could leave comments to posts, and also “like” them, by using a thumbs-up feature to explicitly express a generic, rather underspecified, approval  A “like” could thus mean “I like what you said", but also “I like that you bring up such topic (though I find the content of the article you linked annoying)" 
In February 2016, after a short trial, Facebook made a more explicit reaction feature available world-wide  Rather than allowing for the underspecified “like” as the only wordless response to a post, a set of six more specific reactions was introduced, as shown in Figure FIGREF1 : Like, Love, Haha, Wow, Sad and Angry  We use such reactions as proxies for emotion labels associated to posts 
We collected Facebook posts and their corresponding reactions from public pages using the Facebook API, which we accessed via the Facebook-sdk python library  We chose different pages (and therefore domains and stances), aiming at a balanced and varied dataset, but we did so mainly based on intuition (see Section SECREF4 ) and with an eye to the nature of the datasets available for evaluation (see Section SECREF5 )  The choice of which pages to select posts from is far from trivial, and we believe this is actually an interesting aspect of our approach, as by using different Facebook pages one can intrinsically tackle the domain-adaptation problem (See Section SECREF6 for further discussion on this)  The final collection of Facebook pages for the experiments described in this paper is as follows: FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney 
Note that thankful was only available during specific time spans related to certain events, as Mother's Day in May 2016 
For each page, we downloaded the latest 1000 posts, or the maximum available if there are fewer, from February 2016, retrieving the counts of reactions for each post  The output is a JSON file containing a list of dictionaries with a timestamp, the post and a reaction vector with frequency values, which indicate how many users used that reaction in response to the post (Figure FIGREF3 )  The resulting emotion vectors must then be turned into an emotion label 
In the context of this experiment, we made the simple decision of associating to each post the emotion with the highest count, ignoring like as it is the default and most generic reaction people tend to use  Therefore, for example, to the first post in Figure FIGREF3 , we would associate the label sad, as it has the highest score (284) among the meaningful emotions we consider, though it also has non-zero scores for other emotions  At this stage, we didn't perform any other entropy-based selection of posts, to be investigated in future work 
Emotion datasets
Three datasets annotated with emotions are commonly used for the development and evaluation of emotion detection systems, namely the Affective Text dataset, the Fairy Tales dataset, and the ISEAR dataset  In order to compare our performance to state-of-the-art results, we have used them as well  In this Section, in addition to a description of each dataset, we provide an overview of the emotions used, their distribution, and how we mapped them to those we obtained from Facebook posts in Section SECREF7   A summary is provided in Table TABREF8 , which also shows, in the bottom row, what role each dataset has in our experiments: apart from the development portion of the Affective Text, which we used to develop our models (Section SECREF4 ), all three have been used as benchmarks for our evaluation 
Affective Text dataset
Task 14 at SemEval 2007 BIBREF7 was concerned with the classification of emotions and valence in news headlines  The headlines where collected from several news websites including Google news, The New York Times, BBC News and CNN  The used emotion labels were Anger, Disgust, Fear, Joy, Sadness, Surprise, in line with the six basic emotions of Ekman's standard model BIBREF8   Valence was to be determined as positive or negative  Classification of emotion and valence were treated as separate tasks  Emotion labels were not considered as mututally exclusive, and each emotion was assigned a score from 0 to 100  Training/developing data amounted to 250 annotated headlines (Affective development), while systems were evaluated on another 1000 (Affective test)  Evaluation was done using two different methods: a fine-grained evaluation using Pearson's r to measure the correlation between the system scores and the gold standard; and a coarse-grained method where each emotion score was converted to a binary label, and precision, recall, and f-score were computed to assess performance  As it is done in most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , we also treat this as a classification problem (coarse-grained)  This dataset has been extensively used for the evaluation of various unsupervised methods BIBREF2 , but also for testing different supervised learning techniques and feature portability BIBREF10  
Fairy Tales dataset
This is a dataset collected by alm2008affect, where about 1,000 sentences from fairy tales (by B  Potter, H C  Andersen and Grimm) were annotated with the same six emotions of the Affective Text dataset, though with different names: Angry, Disgusted, Fearful, Happy, Sad, and Surprised  In most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , only sentences where all annotators agreed are used, and the labels angry and disgusted are merged  We adopt the same choices 
ISEAR
The ISEAR (International Survey on Emotion Antecedents and Reactions BIBREF11 , BIBREF12 ) is a dataset created in the context of a psychology project of the 1990s, by collecting questionnaires answered by people with different cultural backgrounds  The main aim of this project was to gather insights in cross-cultural aspects of emotional reactions  Student respondents, both psychologists and non-psychologists, were asked to report situations in which they had experienced all of seven major emotions (joy, fear, anger, sadness, disgust, shame and guilt)  In each case, the questions covered the way they had appraised a given situation and how they reacted  The final dataset contains reports by approximately 3000 respondents from all over the world, for a total of 7665 sentences labelled with an emotion, making this the largest dataset out of the three we use 
Overview of datasets and emotions
We summarise datasets and emotion distribution from two viewpoints  First, because there are different sets of emotions labels in the datasets and Facebook data, we need to provide a mapping and derive a subset of emotions that we are going to use for the experiments  This is shown in Table TABREF8 , where in the “Mapped” column we report the final emotions we use in this paper: anger, joy, sadness, surprise  All labels in each dataset are mapped to these final emotions, which are therefore the labels we use for training and testing our models 
Second, the distribution of the emotions for each dataset is different, as can be seen in Figure FIGREF9  
In Figure FIGREF9 we also provide the distribution of the emotions anger, joy, sadness, surprise per Facebook page, in terms of number of posts (recall that we assign to a post the label corresponding to the majority emotion associated to it, see Section SECREF2 )

ground_truth: Unanswerable

LF-MMI Attention
Seq2Seq 
RNN-T 
Char E2E LF-MMI 
Phone E2E LF-MMI 
CTC + Gram-CTC

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 baselines?
Context 4: Introduction
Distributed word representations, commonly referred to as word embeddings BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , serve as elementary building blocks in the course of algorithm design for an expanding range of applications in natural language processing (NLP), including named entity recognition BIBREF4 , BIBREF5 , parsing BIBREF6 , sentiment analysis BIBREF7 , BIBREF8 , and word-sense disambiguation BIBREF9   Although the empirical utility of word embeddings as an unsupervised method for capturing the semantic or syntactic features of a certain word as it is used in a given lexical resource is well-established BIBREF10 , BIBREF11 , BIBREF12 , an understanding of what these features mean remains an open problem BIBREF13 , BIBREF14 and as such word embeddings mostly remain a black box  It is desirable to be able to develop insight into this black box and be able to interpret what it means, while retaining the utility of word embeddings as semantically-rich intermediate representations  Other than the intrinsic value of this insight, this would not only allow us to explain and understand how algorithms work BIBREF15 , but also set a ground that would facilitate the design of new algorithms in a more deliberate way 
Recent approaches to generating word embeddings (e g  BIBREF0 , BIBREF2 ) are rooted linguistically in the field of distributed semantics BIBREF16 , where words are taken to assume meaning mainly by their degree of interaction (or lack thereof) with other words in the lexicon BIBREF17 , BIBREF18   Under this paradigm, dense, continuous vector representations are learned in an unsupervised manner from a large corpus, using the word cooccurrence statistics directly or indirectly, and such an approach is shown to result in vector representations that mathematically capture various semantic and syntactic relations between words BIBREF0 , BIBREF2 , BIBREF3   However, the dense nature of the learned embeddings obfuscate the distinct concepts encoded in the different dimensions, which renders the resulting vectors virtually uninterpretable  The learned embeddings make sense only in relation to each other and their specific dimensions do not carry explicit information that can be interpreted  However, being able to interpret a word embedding would illuminate the semantic concepts implicitly represented along the various dimensions of the embedding, and reveal its hidden semantic structures 
In the literature, researchers tackled interpretability problem of the word embeddings using different approaches  Several researchers BIBREF19 , BIBREF20 , BIBREF21 proposed algorithms based on non-negative matrix factorization (NMF) applied to cooccurrence variant matrices  Other researchers suggested to obtain interpretable word vectors from existing uninterpretable word vectors by applying sparse coding BIBREF22 , BIBREF23 , by training a sparse auto-encoder to transform the embedding space BIBREF24 , by rotating the original embeddings BIBREF25 , BIBREF26 or by applying transformations based on external semantic datasets BIBREF27  
Although the above-mentioned approaches provide better interpretability that is measured using a particular method such as word intrusion test, usually the improved interpretability comes with a cost of performance in the benchmark tests such as word similarity or word analogy  One possible explanation for this performance decrease is that the proposed transformations from the original embedding space distort the underlying semantic structure constructed by the original embedding algorithm  Therefore, it can be claimed that a method that learns dense and interpretable word embeddings without inflicting any damage to the underlying semantic learning mechanism is the key to achieve both high performing and interpretable word embeddings 
Especially after the introduction of the word2vec algorithm by Mikolov BIBREF0 , BIBREF1 , there has been a growing interest in algorithms that generate improved word representations under some performance metric  Significant effort is spent on appropriately modifying the objective functions of the algorithms in order to incorporate knowledge from external resources, with the purpose of increasing the performance of the resulting word representations BIBREF28 , BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37   Inspired by the line of work reported in these studies, we propose to use modified objective functions for a different purpose: learning more interpretable dense word embeddings  By doing this, we aim to incorporate semantic information from an external lexical resource into the word embedding so that the embedding dimensions are aligned along predefined concepts  This alignment is achieved by introducing a modification to the embedding learning process  In our proposed method, which is built on top of the GloVe algorithm BIBREF2 , the cost function for any one of the words of concept word-groups is modified by the introduction of an additive term to the cost function  Each embedding vector dimension is first associated with a concept  For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to  For words that do not belong to any one of the word-groups, the cost term is left untouched  Specifically, Roget's Thesaurus BIBREF38 , BIBREF39 is used to derive the concepts and concept word-groups to be used as the external lexical resource for our proposed method  We quantitatively demonstrate the increase in interpretability by using the measure given in BIBREF27 , BIBREF40 as well as demonstrating qualitative results  We also show that the semantic structure of the original embedding has not been harmed in the process since there is no performance loss with standard word-similarity or word-analogy tests 
The paper is organized as follows  In Section SECREF2 , we discuss previous studies related to our work under two main categories: interpretability of word embeddings and joint-learning frameworks where the objective function is modified  In Section SECREF3 , we present the problem framework and provide the formulation within the GloVe BIBREF2 algorithm setting  In Section SECREF4 where our approach is proposed, we motivate and develop a modification to the original objective function with the aim of increasing representation interpretability  In Section SECREF5 , experimental results are provided and the proposed method is quantitatively and qualitatively evaluated  Additionally, in Section SECREF5 , results demonstrating the extent to which the original semantic structure of the embedding space is affected are presented by using word-analogy and word-similarity tests  We conclude the paper in Section SECREF6  
Related Work
Methodologically, our work is related to prior studies that aim to obtain “improved” word embeddings using external lexical resources, under some performance metric  Previous work in this area can be divided into two main categories: works that i) modify the word embedding learning algorithm to incorporate lexical information, ii) operate on pre-trained embeddings with a post-processing step 
Among works that follow the first approach, BIBREF28 extend the Skip-Gram model by incorporating the word similarity relations extracted from the Paraphrase Database (PPDB) and WordNet BIBREF29 , into the Skip-Gram predictive model as an additional cost term  In BIBREF30 , the authors extend the CBOW model by considering two types of semantic information, termed relational and categorical, to be incorporated into the embeddings during training  For the former type of semantic information, the authors propose the learning of explicit vectors for the different relations extracted from a semantic lexicon such that the word pairs that satisfy the same relation are distributed more homogeneously  For the latter, the authors modify the learning objective such that some weighted average distance is minimized for words under the same semantic category  In BIBREF31 , the authors represent the synonymy and hypernymy-hyponymy relations in terms of inequality constraints, where the pairwise similarity rankings over word triplets are forced to follow an order extracted from a lexical resource  Following their extraction from WordNet, the authors impose these constraints in the form of an additive cost term to the Skip-Gram formulation  Finally, BIBREF32 builds on top of the GloVe algorithm by introducing a regularization term to the objective function that encourages the vector representations of similar words as dictated by WordNet to be similar as well 
Turning our attention to the post-processing approach for enriching word embeddings with external lexical knowledge, BIBREF33 has introduced the retrofitting algorithm that acts on pre-trained embeddings such as Skip-Gram or GloVe  The authors propose an objective function that aims to balance out the semantic information captured in the pre-trained embeddings with the constraints derived from lexical resources such as WordNet, PPDB and FrameNet  One of the models proposed in BIBREF34 extends the retrofitting approach to incorporate the word sense information from WordNet  Similarly, BIBREF35 creates multi-sense embeddings by gathering the word sense information from a lexical resource and learning to decompose the pre-trained embeddings into a convex combination of sense embeddings  In BIBREF36 , the authors focus on improving word embeddings for capturing word similarity, as opposed to mere relatedness  To this end, they introduce the counter-fitting technique which acts on the input word vectors such that synonymous words are attracted to one another whereas antonymous words are repelled, where the synonymy-antonymy relations are extracted from a lexical resource  More recently, the ATTRACT-REPEL algorithm proposed by BIBREF37 improves on counter-fitting by a formulation which imparts the word vectors with external lexical information in mini-batches 
Most of the studies discussed above ( BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF36 , BIBREF37 ) report performance improvements in benchmark tests such as word similarity or word analogy, while BIBREF29 uses a different analysis method (mean reciprocal rank)  In sum, the literature is rich with studies aiming to obtain word embeddings that perform better under specific performance metrics  However, less attention has been directed to the issue of interpretability of the word embeddings  In the literature, the problem of interpretability has been tackled using different approaches  BIBREF19 proposed non-negative matrix factorization (NMF) for learning sparse, interpretable word vectors from co-occurrence variant matrices where the resulting vector space is called non-negative sparse embeddigns (NNSE)  However, since NMF methods require maintaining a global matrix for learning, they suffer from memory and scale issue  This problem has been addressed in BIBREF20 where an online method of learning interpretable word embeddings from corpora using a modified version of skip-gram model BIBREF0 is proposed  As a different approach, BIBREF21 combined text-based similarity information among words with brain activity based similarity information to improve interpretability using joint non-negative sparse embedding (JNNSE) 
A common alternative approach for learning interpretable embeddings is to learn transformations that map pre-trained state-of-the-art embeddings to new interpretable semantic spaces  To obtain sparse, higher dimensional and more interpretable vector spaces, BIBREF22 and BIBREF23 use sparse coding on conventional dense word embeddings  However, these methods learn the projection vectors that are used for the transformation from the word embeddings without supervision  For this reason, labels describing the corresponding semantic categories cannot be provided  An alternative approach was proposed in BIBREF25 , where orthogonal transformations were utilized to increase interpretability while preserving the performance of the underlying embedding  However, BIBREF25 has also shown that total interpretability of an embedding is kept constant under any orthogonal transformation and it can only be redistributed across the dimensions  Rotation algorithms based on exploratory factor analysis (EFA) to preserve the performance of the original word embeddings while improving their interpretability was proposed in BIBREF26   BIBREF24 proposed to deploy a sparse auto-encoder using pre-trained dense word embeddings to improve interpretability  More detailed investigation of semantic structure and interpretability of word embeddings can be found in BIBREF27 , where a metric was proposed to quantitatively measure the degree of interpretability already present in the embedding vector spaces 
Previous works on interpretability mentioned above, except BIBREF21 , BIBREF27 and our proposed method, do not need external resources, utilization of which has both advantages and disadvantages  Methods that do not use external resources require fewer resources but they also lack the aid of information extracted from these resources 
Problem Description
For the task of unsupervised word embedding extraction, we operate on a discrete collection of lexical units (words) INLINEFORM0 that is part of an input corpus INLINEFORM1 , with number of tokens INLINEFORM2 , sourced from a vocabulary INLINEFORM3 of size INLINEFORM4   In the setting of distributional semantics, the objective of a word embedding algorithm is to maximize some aggregate utility over the entire corpus so that some measure of “closeness” is maximized for pairs of vector representations INLINEFORM14 for words which, on the average, appear in proximity to one another  In the GloVe algorithm BIBREF2 , which we base our improvements upon, the following objective function is considered: DISPLAYFORM0
In ( EQREF6 ), INLINEFORM0 and INLINEFORM1 stand for word and context vector representations, respectively, for words INLINEFORM2 and INLINEFORM3 , while INLINEFORM4 represents the (possibly weighted) cooccurrence count for the word pair INLINEFORM5   Intuitively, ( EQREF6 ) represents the requirement that if some word INLINEFORM6 occurs often enough in the context (or vicinity) of another word INLINEFORM7 , then the corresponding word representations should have a large enough inner product in keeping with their large INLINEFORM8 value, up to some bias terms INLINEFORM9 ; and vice versa  INLINEFORM10 in ( EQREF6 ) is used as a discounting factor that prohibits rare cooccurrences from disproportionately influencing the resulting embeddings 
The objective ( EQREF6 ) is minimized using stochastic gradient descent by iterating over the matrix of cooccurrence records INLINEFORM0   In the GloVe algorithm, for a given word INLINEFORM1 , the final word representation is taken to be the average of the two intermediate vector representations obtained from ( EQREF6 ); i e, INLINEFORM2   In the next section, we detail the enhancements made to ( EQREF6 ) for the purposes of enhanced interpretability, using the aforementioned framework as our basis 
Imparting Interpretability
Our approach falls into a joint-learning framework where the distributional information extracted from the corpus is allowed to fuse with the external lexicon-based information  Word-groups extracted from Roget's Thesaurus are directly mapped to individual dimensions of word embeddings  Specifically, the vector representations of words that belong to a particular group are encouraged to have deliberately increased values in a particular dimension that corresponds to the word-group under consideration  This can be achieved by modifying the objective function of the embedding algorithm to partially influence vector representation distributions across their dimensions over an input vocabulary  To do this, we propose the following modification to the GloVe objective in ( EQREF6 ): rCl J = i,j=1V f(Xij)[ (wiTwj + bi + bj -Xij)2
+ k(l=1D INLINEFORM0 iFl g(wi,l) + l=1D INLINEFORM1 j Fl g(wj,l) ) ]  In ( SECREF4 ), INLINEFORM2 denotes the indices for the elements of the INLINEFORM3 th concept word-group which we wish to assign in the vector dimension INLINEFORM4   The objective ( SECREF4 ) is designed as a mixture of two individual cost terms: the original GloVe cost term along with a second term that encourages embedding vectors of a given concept word-group to achieve deliberately increased values along an associated dimension INLINEFORM5   The relative weight of the second term is controlled by the parameter INLINEFORM6   The simultaneous minimization of both objectives ensures that words that are similar to, but not included in, one of these concept word-groups are also “nudged” towards the associated dimension INLINEFORM7   The trained word vectors are thus encouraged to form a distribution where the individual vector dimensions align with certain semantic concepts represented by a collection of concept word-groups, one assigned to each vector dimension  To facilitate this behaviour, ( SECREF4 ) introduces a monotone decreasing function INLINEFORM8 defined as INLINEFORM9
which serves to increase the total cost incurred if the value of the INLINEFORM0 th dimension for the two vector representations INLINEFORM1 and INLINEFORM2 for a concept word INLINEFORM3 with INLINEFORM4 fails to be large enough  INLINEFORM5 is also shown in Fig  FIGREF7  
The objective ( SECREF4 ) is minimized using stochastic gradient descent over the cooccurrence records INLINEFORM0   Intuitively, the terms added to ( SECREF4 ) in comparison with ( EQREF6 ) introduce the effect of selectively applying a positive step-type input to the original descent updates of ( EQREF6 ) for concept words along their respective vector dimensions, which influences the dimension value in the positive direction  The parameter INLINEFORM1 in ( SECREF4 ) allows for the adjustment of the magnitude of this influence as needed 
In the next section, we demonstrate the feasibility of this approach by experiments with an example collection of concept word-groups extracted from Roget's Thesaurus 
Experiments and Results
We first identified 300 concepts, one for each dimension of the 300-dimensional vector representation, by employing Roget's Thesaurus  This thesaurus follows a tree structure which starts with a Root node that contains all the words and phrases in the thesaurus  The root node is successively split into Classes and Sections, which are then (optionally) split into Subsections of various depths, finally ending in Categories, which constitute the smallest unit of word/phrase collections in the structure  The actual words and phrases descend from these Categories, and make up the leaves of the tree structure  We note that a given word typically appears in multiple categories corresponding to the different senses of the word  We constructed concept word-groups from Roget's Thesaurus as follows: We first filtered out the multi-word phrases and the relatively obscure terms from the thesaurus  The obscure terms were identified by checking them against a vocabulary extracted from Wikipedia  We then obtained 300 word-groups as the result of a partitioning operation applied to the subtree that ends with categories as its leaves  The partition boundaries, hence the resulting word-groups, can be chosen in many different ways  In our proposed approach, we have chosen to determine this partitioning by traversing this tree structure from the root node in breadth-first order, and by employing a parameter INLINEFORM0 for the maximum size of a node  Here, the size of a node is defined as the number of unique words that ever-descend from that node  During the traversal, if the size of a given node is less than this threshold, we designate the words that ultimately descend from that node as a concept word-group  Otherwise, if the node has children, we discard the node, and queue up all its children for further consideration  If this node does not have any children, on the other hand, the node is truncated to INLINEFORM1 elements with the highest frequency-ranks, and the resulting words are designated as a concept word-group  We note that the choice of INLINEFORM2 greatly affects the resulting collection of word-groups: Excessively large values result in few word-groups that greatly overlap with one another, while overly small values result in numerous tiny word-groups that fail to adequately represent a concept  We experimentally determined that a INLINEFORM3 value of 452 results in the most healthy number of relatively large word-groups (113 groups with size INLINEFORM4 100), while yielding a preferably small overlap amongst the resulting word-groups (with average overlap size not exceeding 3 words)  A total of 566 word-groups were thus obtained  259 smallest word-groups (with size INLINEFORM5 38) were discarded to bring down the number of word-groups to 307  Out of these, 7 groups with the lowest median frequency-rank were further discarded, which yields the final 300 concept word-groups used in the experiments  We present some of the resulting word-groups in Table TABREF9  
By using the concept word-groups, we have trained the GloVe algorithm with the proposed modification given in Section SECREF4 on a snapshot of English Wikipedia measuring 8GB in size, with the stop-words filtered out  Using the parameters given in Table TABREF10 , this resulted in a vocabulary size of 287,847  For the weighting parameter in Eq  SECREF4 , we used a value of INLINEFORM0   The algorithm was trained over 20 iterations  The GloVe algorithm without any modifications was also trained as a baseline with the same parameters  In addition to the original GloVe algorithm, we compare our proposed method with previous studies that aim to obtain interpretable word vectors

ground_truth: SVM with unigram, bigram, and trigram features, SVM with average word embedding, SVM with average transformed word embeddings, CNN, ecurrent Convolutional Neural Networks, SVM and deep learning models with comment information

SVM with unigram, bigram, trigram features, with average word embedding, with average transformed word embeddings, CNN and RCNN, SVM, CNN, RCNN with comment information

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 Facebook pages did they look at?
Context 3:  We can observe that for example pages about news tend to have more sadness and anger posts, while pages about cooking and tv-shows have a high percentage of joy posts  We will use this information to find the best set of pages for a given target domain (see Section SECREF5 ) 
Model
There are two main decisions to be taken in developing our model: (i) which Facebook pages to select as training data, and (ii) which features to use to train the model, which we discuss below  Specifically, we first set on a subset of pages and then experiment with features  Further exploration of the interaction between choice of pages and choice of features is left to future work, and partly discussed in Section SECREF6   For development, we use a small portion of the Affective data set described in Section SECREF4 , that is the portion that had been released as development set for SemEval's 2007 Task 14 BIBREF7 , which contains 250 annotated sentences (Affective development, Section SECREF4 )  All results reported in this section are on this dataset  The test set of Task 14 as well as the other two datasets described in Section SECREF3 will be used to evaluate the final models (Section SECREF4 ) 
Selecting Facebook pages
Although page selection is a crucial ingredient of this approach, which we believe calls for further and deeper, dedicated investigation, for the experiments described here we took a rather simple approach  First, we selected the pages that would provide training data based on intuition and availability, then chose different combinations according to results of a basic model run on development data, and eventually tested feature combinations, still on the development set 
For the sake of simplicity and transparency, we first trained an SVM with a simple bag-of-words model and default parameters as per the Scikit-learn implementation BIBREF13 on different combinations of pages  Based on results of the attempted combinations as well as on the distribution of emotions in the development dataset (Figure FIGREF9 ), we selected a best model (B-M), namely the combined set of Time, The Guardian and Disney, which yields the highest results on development data  Time and The Guardian perform well on most emotions but Disney helps to boost the performance for the Joy class 
Features
In selecting appropriate features, we mainly relied on previous work and intuition  We experimented with different combinations, and all tests were still done on Affective development, using the pages for the best model (B-M) described above as training data  Results are in Table TABREF20   Future work will further explore the simultaneous selection of features and page combinations 
We use a set of basic text-based features to capture the emotion class  These include a tf-idf bag-of-words feature, word (2-3) and character (2-5) ngrams, and features related to the presence of negation words, and to the usage of punctuation 
This feature is used in all unsupervised models as a source of information, and we mainly include it to assess its contribution, but eventually do not use it in our final model 
We used the NRC10 Lexicon because it performed best in the experiments by BIBREF10 , which is built around the emotions anger, anticipation, disgust, fear, joy, sadness, and surprise, and the valence values positive and negative  For each word in the lexicon, a boolean value indicating presence or absence is associated to each emotion  For a whole sentence, a global score per emotion can be obtained by summing the vectors for all content words of that sentence included in the lexicon, and used as feature 
As additional feature, we also included Word Embeddings, namely distributed representations of words in a vector space, which have been exceptionally successful in boosting performance in a plethora of NLP tasks  We use three different embeddings:
Google embeddings: pre-trained embeddings trained on Google News and obtained with the skip-gram architecture described in BIBREF14   This model contains 300-dimensional vectors for 3 million words and phrases 
Facebook embeddings: embeddings that we trained on our scraped Facebook pages for a total of 20,000 sentences  Using the gensim library BIBREF15 , we trained the embeddings with the following parameters: window size of 5, learning rate of 0 01 and dimensionality of 100  We filtered out words with frequency lower than 2 occurrences 
Retrofitted embeddings: Retrofitting BIBREF16 has been shown as a simple but efficient way of informing trained embeddings with additional information derived from some lexical resource, rather than including it directly at the training stage, as it's done for example to create sense-aware BIBREF17 or sentiment-aware BIBREF18 embeddings  In this work, we retrofit general embeddings to include information about emotions, so that emotion-similar words can get closer in space  Both the Google as well as our Facebook embeddings were retrofitted with lexical information obtained from the NRC10 Lexicon mentioned above, which provides emotion-similarity for each token  Note that differently from the previous two types of embeddings, the retrofitted ones do rely on handcrafted information in the form of a lexical resource 
Results on development set
We report precision, recall, and f-score on the development set  The average f-score is reported as micro-average, to better account for the skewed distribution of the classes as well as in accordance to what is usually reported for this task BIBREF19  
From Table TABREF20 we draw three main observations  First, a simple tf-idf bag-of-word mode works already very well, to the point that the other textual and lexicon-based features don't seem to contribute to the overall f-score (0 368), although there is a rather substantial variation of scores per class  Second, Google embeddings perform a lot better than Facebook embeddings, and this is likely due to the size of the corpus used for training  Retrofitting doesn't seem to help at all for the Google embeddings, but it does boost the Facebook embeddings, leading to think that with little data, more accurate task-related information is helping, but corpus size matters most  Third, in combination with embeddings, all features work better than just using tf-idf, but removing the Lexicon feature, which is the only one based on hand-crafted resources, yields even better results  Then our best model (B-M) on development data relies entirely on automatically obtained information, both in terms of training data as well as features 
Results
In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3  
Our B-M model relies on subsets of Facebook pages for training, which were chosen according to their performance on the development set as well as on the observation of emotions distribution on different pages and in the different datasets, as described in Section SECREF4   The feature set we use is our best on the development set, namely all the features plus Google-based embeddings, but excluding the lexicon  This makes our approach completely independent of any manual annotation or handcrafted resource  Our model's performance is compared to the following systems, for which results are reported in the referred literature  Please note that no other existing model was re-implemented, and results are those reported in the respective papers 
Discussion, conclusions and future work
We have explored the potential of using Facebook reactions in a distant supervised setting to perform emotion classification  The evaluation on standard benchmarks shows that models trained as such, especially when enhanced with continuous vector representations, can achieve competitive results without relying on any handcrafted resource  An interesting aspect of our approach is the view to domain adaptation via the selection of Facebook pages to be used as training data 
We believe that this approach has a lot of potential, and we see the following directions for improvement  Feature-wise, we want to train emotion-aware embeddings, in the vein of work by tang:14, and iacobacci2015sensembed  Retrofitting FB-embeddings trained on a larger corpus might also be successful, but would rely on an external lexicon 
The largest room for yielding not only better results but also interesting insights on extensions of this approach lies in the choice of training instances, both in terms of Facebook pages to get posts from, as well as in which posts to select from the given pages  For the latter, one could for example only select posts that have a certain length, ignore posts that are only quotes or captions to images, or expand posts by including content from linked html pages, which might provide larger and better contexts BIBREF23   Additionally, and most importantly, one could use an entropy-based measure to select only posts that have a strong emotion rather than just considering the majority emotion as training label  For the former, namely the choice of Facebook pages, which we believe deserves the most investigation, one could explore several avenues, especially in relation to stance-based issues BIBREF24   In our dataset, for example, a post about Chile beating Colombia in a football match during the Copa America had very contradictory reactions, depending on which side readers would cheer for  Similarly, the very same political event, for example, would get very different reactions from readers if it was posted on Fox News or The Late Night Show, as the target audience is likely to feel very differently about the same issue  This also brings up theoretical issues related more generally to the definition of the emotion detection task, as it's strongly dependent on personal traits of the audience  Also, in this work, pages initially selected on availability and intuition were further grouped into sets to make training data according to performance on development data, and label distribution  Another criterion to be exploited would be vocabulary overlap between the pages and the datasets 
Lastly, we could develop single models for each emotion, treating the problem as a multi-label task  This would even better reflect the ambiguity and subjectivity intrinsic to assigning emotions to text, where content could be at same time joyful or sad, depending on the reader 
Acknowledgements
In addition to the anonymous reviewers, we want to thank Lucia Passaro and Barbara Plank for insightful discussions, and for providing comments on draft versions of this paper 

ground_truth: FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney

FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney.