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. In what language are the tweets?
Context 1: Introduction
Offensive content has become pervasive in social media and a reason of concern for government organizations, online communities, and social media platforms  One of the most common strategies to tackle the problem is to train systems capable of recognizing offensive content, which then can be deleted or set aside for human moderation  In the last few years, there have been several studies published on the application of computational methods to deal with this problem  Most prior work focuses on a different aspect of offensive language such as abusive language BIBREF0 , BIBREF1 , (cyber-)aggression BIBREF2 , (cyber-)bullying BIBREF3 , BIBREF4 , toxic comments INLINEFORM0 , hate speech BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , and offensive language BIBREF11   Prior work has focused on these aspects of offensive language in Twitter BIBREF3 , BIBREF7 , BIBREF8 , BIBREF11 , Wikipedia comments, and Facebook posts BIBREF2  
Recently, Waseem et  al  ( BIBREF12 ) acknowledged the similarities among prior work and discussed the need for a typology that differentiates between whether the (abusive) language is directed towards a specific individual or entity or towards a generalized group and whether the abusive content is explicit or implicit  Wiegand et al  ( BIBREF11 ) followed this trend as well on German tweets  In their evaluation, they have a task to detect offensive vs not offensive tweets and a second task for distinguishing between the offensive tweets as profanity, insult, or abuse  However, no prior work has explored the target of the offensive language, which is important in many scenarios, e g , when studying hate speech with respect to a specific target 
Therefore, we expand on these ideas by proposing a a hierarchical three-level annotation model that encompasses:
Using this annotation model, we create a new large publicly available dataset of English tweets  The key contributions of this paper are as follows:
Related Work
Different abusive and offense language identification sub-tasks have been explored in the past few years including aggression identification, bullying detection, hate speech, toxic comments, and offensive language 
Aggression identification: The TRAC shared task on Aggression Identification BIBREF2 provided participants with a dataset containing 15,000 annotated Facebook posts and comments in English and Hindi for training and validation  For testing, two different sets, one from Facebook and one from Twitter were provided  Systems were trained to discriminate between three classes: non-aggressive, covertly aggressive, and overtly aggressive 
Bullying detection: Several studies have been published on bullying detection  One of them is the one by xu2012learning which apply sentiment analysis to detect bullying in tweets  xu2012learning use topic models to to identify relevant topics in bullying  Another related study is the one by dadvar2013improving which use user-related features such as the frequency of profanity in previous messages to improve bullying detection 
Hate speech identification: It is perhaps the most widespread abusive language detection sub-task  There have been several studies published on this sub-task such as kwok2013locate and djuric2015hate who build a binary classifier to distinguish between `clean' comments and comments containing hate speech and profanity  More recently, Davidson et al  davidson2017automated presented the hate speech detection dataset containing over 24,000 English tweets labeled as non offensive, hate speech, and profanity 
Offensive language: The GermEval BIBREF11 shared task focused on Offensive language identification in German tweets  A dataset of over 8,500 annotated tweets was provided for a course-grained binary classification task in which systems were trained to discriminate between offensive and non-offensive tweets and a second task where the organizers broke down the offensive class into three classes: profanity, insult, and abuse 
Toxic comments: The Toxic Comment Classification Challenge was an open competition at Kaggle which provided participants with comments from Wikipedia labeled in six classes: toxic, severe toxic, obscene, threat, insult, identity hate 
While each of these sub-tasks tackle a particular type of abuse or offense, they share similar properties and the hierarchical annotation model proposed in this paper aims to capture this  Considering that, for example, an insult targeted at an individual is commonly known as cyberbulling and that insults targeted at a group are known as hate speech, we pose that OLID's hierarchical annotation model makes it a useful resource for various offensive language identification sub-tasks 
Hierarchically Modelling Offensive Content
In the OLID dataset, we use a hierarchical annotation model split into three levels to distinguish between whether language is offensive or not (A), and type (B) and target (C) of the offensive language  Each level is described in more detail in the following subsections and examples are shown in Table TABREF10  
Level A: Offensive language Detection
Level A discriminates between offensive (OFF) and non-offensive (NOT) tweets 
Not Offensive (NOT): Posts that do not contain offense or profanity;
Offensive (OFF): We label a post as offensive if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct  This category includes insults, threats, and posts containing profane language or swear words 
Level B: Categorization of Offensive Language
Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats 
Targeted Insult (TIN): Posts which contain an insult/threat to an individual, group, or others (see next layer);
Untargeted (UNT): Posts containing non-targeted profanity and swearing  Posts with general profanity are not targeted, but they contain non-acceptable language 
Level C: Offensive Language Target Identification
Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH) 
Individual (IND): Posts targeting an individual  It can be a a famous person, a named individual or an unnamed participant in the conversation  Insults and threats targeted at individuals are often defined as cyberbulling 
Group (GRP): The target of these offensive posts is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or other common characteristic  Many of the insults and threats targeted at a group correspond to what is commonly understood as hate speech 
Other (OTH): The target of these offensive posts does not belong to any of the previous two categories (e g  an organization, a situation, an event, or an issue) 
Data Collection
The data included in OLID has been collected from Twitter  We retrieved the data using the Twitter API by searching for keywords and constructions that are often included in offensive messages, such as `she is' or `to:BreitBartNews'  We carried out a first round of trial annotation of 300 instances with six experts  The goal of the trial annotation was to 1) evaluate the proposed tagset; 2) evaluate the data retrieval method; and 3) create a gold standard with instances that could be used as test questions in the training and test setting annotation which was carried out using crowdsourcing  The breakdown of keywords and their offensive content in the trial data of 300 tweets is shown in Table TABREF14   We included a left (@NewYorker) and far-right (@BreitBartNews) news accounts because there tends to be political offense in the comments  One of the best offensive keywords was tweets that were flagged as not being safe by the Twitter `safe' filter (the `-' indicates `not safe')  The vast majority of content on Twitter is not offensive so we tried different strategies to keep a reasonable number of tweets in the offensive class amounting to around 30% of the dataset including excluding some keywords that were not high in offensive content such as `they are` and `to:NewYorker`  Although `he is' is lower in offensive content we kept it as a keyword to avoid gender bias  In addition to the keywords in the trial set, we searched for more political keywords which tend to be higher in offensive content, and sampled our dataset such that 50% of the the tweets come from political keywords and 50% come from non-political keywords  In addition to the keywords `gun control', and `to:BreitbartNews', political keywords used to collect these tweets are `MAGA', `antifa', `conservative' and `liberal'  We computed Fliess' INLINEFORM0 on the trial set for the five annotators on 21 of the tweets  INLINEFORM1 is  83 for Layer A (OFF vs NOT) indicating high agreement  As to normalization and anonymization, no user metadata or Twitter IDs have been stored, and URLs and Twitter mentions have been substituted to placeholders  We follow prior work in related areas (burnap2015cyber,davidson2017automated) and annotate our data using crowdsourcing using the platform Figure Eight  We ensure data quality by: 1) we only received annotations from individuals who were experienced in the platform; and 2) we used test questions to discard annotations of individuals who did not reach a certain threshold  Each instance in the dataset was annotated by multiple annotators and inter-annotator agreement has been calculated  We first acquired two annotations for each instance  In case of 100% agreement, we considered these as acceptable annotations, and in case of disagreement, we requested more annotations until the agreement was above 66%  After the crowdsourcing annotation, we used expert adjudication to guarantee the quality of the annotation  The breakdown of the data into training and testing for the labels from each level is shown in Table TABREF15  
Experiments and Evaluation
We assess our dataset using traditional and deep learning methods  Our simplest model is a linear SVM trained on word unigrams  SVMs have produced state-of-the-art results for many text classification tasks BIBREF13   We also train a bidirectional Long Short-Term-Memory (BiLSTM) model, which we adapted from the sentiment analysis system of sentimentSystem,rasooli2018cross and altered to predict offensive labels instead  It consists of (1) an input embedding layer, (2) a bidirectional LSTM layer, (3) an average pooling layer of input features  The concatenation of the LSTM's and average pool layer is passed through a dense layer and the output is passed through a softmax function  We set two input channels for the input embedding layers: pre-trained FastText embeddings BIBREF14 , as well as updatable embeddings learned by the model during training  Finally, we also apply a Convolutional Neural Network (CNN) model based on the architecture of BIBREF15 , using the same multi-channel inputs as the above BiLSTM 
Our models are trained on the training data, and evaluated by predicting the labels for the held-out test set  The distribution is described in Table TABREF15   We evaluate and compare the models using the macro-averaged F1-score as the label distribution is highly imbalanced  Per-class Precision (P), Recall (R), and F1-score (F1), also with other averaged metrics are also reported  The models are compared against baselines of predicting all labels as the majority or minority classes 
Offensive Language Detection
The performance on discriminating between offensive (OFF) and non-offensive (NOT) posts is reported in Table TABREF18   We can see that all systems perform significantly better than chance, with the neural models being substantially better than the SVM  The CNN outperforms the RNN model, achieving a macro-F1 score of 0 80 
Categorization of Offensive Language
In this experiment, the two systems were trained to discriminate between insults and threats (TIN) and untargeted (UNT) offenses, which generally refer to profanity  The results are shown in Table TABREF19  
The CNN system achieved higher performance in this experiment compared to the BiLSTM, with a macro-F1 score of 0 69  All systems performed better at identifying target and threats (TIN) than untargeted offenses (UNT) 
Offensive Language Target Identification
The results of the offensive target identification experiment are reported in Table TABREF20   Here the systems were trained to distinguish between three targets: a group (GRP), an individual (IND), or others (OTH)  All three models achieved similar results far surpassing the random baselines, with a slight performance edge for the neural models 
The performance of all systems for the OTH class is 0  This poor performances can be explained by two main factors  First, unlike the two other classes, OTH is a heterogeneous collection of targets  It includes offensive tweets targeted at organizations, situations, events, etc  making it more challenging for systems to learn discriminative properties of this class  Second, this class contains fewer training instances than the other two  There are only 395 instances in OTH, and 1,075 in GRP, and 2,407 in IND 
Conclusion and Future Work
This paper presents OLID, a new dataset with annotation of type and target of offensive language  OLID is the official dataset of the shared task SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) BIBREF16   In OffensEval, each annotation level in OLID is an independent sub-task  The dataset contains 14,100 tweets and is released freely to the research community  To the best of our knowledge, this is the first dataset to contain annotation of type and target of offenses in social media and it opens several new avenues for research in this area  We present baseline experiments using SVMs and neural networks to identify the offensive tweets, discriminate between insults, threats, and profanity, and finally to identify the target of the offensive messages  The results show that this is a challenging task  A CNN-based sentence classifier achieved the best results in all three sub-tasks 
In future work, we would like to make a cross-corpus comparison of OLID and datasets annotated for similar tasks such as aggression identification BIBREF2 and hate speech detection BIBREF8   This comparison is, however, far from trivial as the annotation of OLID is different 
Acknowledgments
The research presented in this paper was partially supported by an ERAS fellowship awarded by the University of Wolverhampton 
Context 2: Introduction
Sarcasm is an intensive, indirect and complex construct that is often intended to express contempt or ridicule   Sarcasm, in speech, is multi-modal, involving tone, body-language and gestures along with linguistic artifacts used in speech  Sarcasm in text, on the other hand, is more restrictive when it comes to such non-linguistic modalities  This makes recognizing textual sarcasm more challenging for both humans and machines 
Sarcasm detection plays an indispensable role in applications like online review summarizers, dialog systems, recommendation systems and sentiment analyzers  This makes automatic detection of sarcasm an important problem  However, it has been quite difficult to solve such a problem with traditional NLP tools and techniques  This is apparent from the results reported by the survey from DBLP:journals/corr/JoshiBC16  The following discussion brings more insights into this 
Consider a scenario where an online reviewer gives a negative opinion about a movie through sarcasm: “This is the kind of movie you see because the theater has air conditioning”  It is difficult for an automatic sentiment analyzer to assign a rating to the movie and, in the absence of any other information, such a system may not be able to comprehend that prioritizing the air-conditioning facilities of the theater over the movie experience indicates a negative sentiment towards the movie  This gives an intuition to why, for sarcasm detection, it is necessary to go beyond textual analysis 
We aim to address this problem by exploiting the psycholinguistic side of sarcasm detection, using cognitive features extracted with the help of eye-tracking  A motivation to consider cognitive features comes from analyzing human eye-movement trajectories that supports the conjecture: Reading sarcastic texts induces distinctive eye movement patterns, compared to literal texts  The cognitive features, derived from human eye movement patterns observed during reading, include two primary feature types:
The cognitive features, along with textual features used in best available sarcasm detectors, are used to train binary classifiers against given sarcasm labels  Our experiments show significant improvement in classification accuracy over the state of the art, by performing such augmentation 
Related Work
Sarcasm, in general, has been the focus of research for quite some time  In one of the pioneering works jorgensen1984test explained how sarcasm arises when a figurative meaning is used opposite to the literal meaning of the utterance  In the word of clark1984pretense, sarcasm processing involves canceling the indirectly negated message and replacing it with the implicated one  giora1995irony, on the other hand, define sarcasm as a mode of indirect negation that requires processing of both negated and implicated messages  ivanko2003context define sarcasm as a six tuple entity consisting of a speaker, a listener, Context, Utterance, Literal Proposition and Intended Proposition and study the cognitive aspects of sarcasm processing 
Computational linguists have previously addressed this problem using rule based and statistical techniques, that make use of : (a) Unigrams and Pragmatic features BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 (b) Stylistic patterns BIBREF4 and patterns related to situational disparity BIBREF5 and (c) Hastag interpretations BIBREF6 , BIBREF7  
Most of the previously done work on sarcasm detection uses distant supervision based techniques (ex: leveraging hashtags) and stylistic/pragmatic features (emoticons, laughter expressions such as “lol” etc)  But, detecting sarcasm in linguistically well-formed structures, in absence of explicit cues or information (like emoticons), proves to be hard using such linguistic/stylistic features alone 
With the advent of sophisticated eye-trackers and electro/magneto-encephalographic (EEG/MEG) devices, it has been possible to delve deep into the cognitive underpinnings of sarcasm understanding  Filik2014, using a series of eye-tracking and EEG experiments try to show that for unfamiliar ironies, the literal interpretation would be computed first  They also show that a mismatch with context would lead to a re-interpretation of the statement, as being ironic  Camblin2007103 show that in multi-sentence passages, discourse congruence has robust effects on eye movements  This also implies that disrupted processing occurs for discourse incongruent words, even though they are perfectly congruous at the sentence level  In our previous work BIBREF8 , we augment cognitive features, derived from eye-movement patterns of readers, with textual features to detect whether a human reader has realized the presence of sarcasm in text or not 
The recent advancements in the literature discussed above, motivate us to explore gaze-based cognition for sarcasm detection  As far as we know, our work is the first of its kind 
Eye-tracking Database for Sarcasm Analysis
Sarcasm often emanates from incongruity BIBREF9 , which enforces the brain to reanalyze it BIBREF10   This, in turn, affects the way eyes move through the text  Hence, distinctive eye-movement patterns may be observed in the case of successful processing of sarcasm in text in contrast to literal texts  This hypothesis forms the crux of our method for sarcasm detection and we validate this using our previously released freely available sarcasm dataset BIBREF8 enriched with gaze information 
Document Description
The database consists of 1,000 short texts, each having 10-40 words  Out of these, 350 are sarcastic and are collected as follows: (a) 103 sentences are from two popular sarcastic quote websites, (b) 76 sarcastic short movie reviews are manually extracted from the Amazon Movie Corpus BIBREF11 by two linguists  (c) 171 tweets are downloaded using the hashtag #sarcasm from Twitter  The 650 non-sarcastic texts are either downloaded from Twitter or extracted from the Amazon Movie Review corpus  The sentences do not contain words/phrases that are highly topic or culture specific  The tweets were normalized to make them linguistically well formed to avoid difficulty in interpreting social media lingo  Every sentence in our dataset carries positive or negative opinion about specific “aspects”  For example, the sentence “The movie is extremely well cast” has positive sentiment about the aspect “cast” 
The annotators were seven graduate students with science and engineering background, and possess good English proficiency  They were given a set of instructions beforehand and are advised to seek clarifications before they proceed  The instructions mention the nature of the task, annotation input method, and necessity of head movement minimization during the experiment 
Task Description
The task assigned to annotators was to read sentences one at a time and label them with with binary labels indicating the polarity (i e , positive/negative)  Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not , to rule out the Priming Effect (i e , if sarcasm is expected beforehand, processing incongruity becomes relatively easier BIBREF12 )  The setup ensures its “ecological validity” in two ways: (1) Readers are not given any clue that they have to treat sarcasm with special attention  This is done by setting the task to polarity annotation (instead of sarcasm detection)  (2) Sarcastic sentences are mixed with non sarcastic text, which does not give prior knowledge about whether the forthcoming text will be sarcastic or not 
The eye-tracking experiment is conducted by following the standard norms in eye-movement research BIBREF13   At a time, one sentence is displayed to the reader along with the “aspect” with respect to which the annotation has to be provided  While reading, an SR-Research Eyelink-1000 eye-tracker (monocular remote mode, sampling rate 500Hz) records several eye-movement parameters like fixations (a long stay of gaze) and saccade (quick jumping of gaze between two positions of rest) and pupil size 
The accuracy of polarity annotation varies between 72%-91% for sarcastic texts and 75%-91% for non-sarcastic text, showing the inherent difficulty of sentiment annotation, when sarcasm is present in the text under consideration  Annotation errors may be attributed to: (a) lack of patience/attention while reading, (b) issues related to text comprehension, and (c) confusion/indecisiveness caused due to lack of context 
For our analysis, we do not discard the incorrect annotations present in the database  Since our system eventually aims to involve online readers for sarcasm detection, it will be hard to segregate readers who misinterpret the text  We make a rational assumption that, for a particular text, most of the readers, from a fairly large population, will be able to identify sarcasm  Under this assumption, the eye-movement parameters, averaged across all readers in our setting, may not be significantly distorted by a few readers who would have failed to identify sarcasm  This assumption is applicable for both regular and multi-instance based classifiers explained in section SECREF6  
Analysis of Eye-movement Data
We observe distinct behavior during sarcasm reading, by analyzing the “fixation duration on the text” (also referred to as “dwell time” in the literature) and “scanpaths” of the readers 
Variation in the Average Fixation Duration per Word
Since sarcasm in text can be expected to induce cognitive load, it is reasonable to believe that it would require more processing time BIBREF14   Hence, fixation duration normalized over total word count should usually be higher for a sarcastic text than for a non-sarcastic one  We observe this for all participants in our dataset, with the average fixation duration per word for sarcastic texts being at least 1 5 times more than that of non-sarcastic texts  To test the statistical significance, we conduct a two-tailed t-test (assuming unequal variance) to compare the average fixation duration per word for sarcastic and non-sarcastic texts  The hypothesized mean difference is set to 0 and the error tolerance limit ( INLINEFORM0 ) is set to 0 05  The t-test analysis, presented in Table TABREF11 , shows that for all participants, a statistically significant difference exists between the average fixation duration per word for sarcasm (higher average fixation duration) and non-sarcasm (lower average fixation duration)  This affirms that the presence of sarcasm affects the duration of fixation on words 
It is important to note that longer fixations may also be caused by other linguistic subtleties (such as difficult words, ambiguity and syntactically complex structures) causing delay in comprehension, or occulomotor control problems forcing readers to spend time adjusting eye-muscles  So, an elevated average fixation duration per word may not sufficiently indicate the presence of sarcasm  But we would also like to share that, for our dataset, when we considered readability (Flesch readability ease-score BIBREF15 ), number of words in a sentence and average character per word along with the sarcasm label as the predictors of average fixation duration following a linear mixed effect model BIBREF16 , sarcasm label turned out to be the most significant predictor with a maximum slope  This indicates that average fixation duration per word has a strong connection with the text being sarcastic, at least in our dataset 
We now analyze scanpaths to gain more insights into the sarcasm comprehension process 
Analysis of Scanpaths
Scanpaths are line-graphs that contain fixations as nodes and saccades as edges; the radii of the nodes represent the fixation duration  A scanpath corresponds to a participant's eye-movement pattern while reading a particular sentence
Context 3: Introduction
Natural Language Processing (NLP) has increasingly attracted the attention of the financial community  This trend can be explained by at least three major factors  The first factor refers to the business perspective  It is the economics of gaining competitive advantage using alternative sources of data and going beyond historical stock prices, thus, trading by analyzing market news automatically  The second factor is the major advancements in the technologies to collect, store, and query massive amounts of user-generated data almost in real-time  The third factor refers to the progress made by the NLP community in understanding unstructured text  Over the last decades the number of studies using NLP for financial forecasting has experienced exponential growth  According to BIBREF0 , until 2008, less than five research articles were published per year mentioning both “stock market” and “text mining” or “sentiment analysis” keywords  In 2012, this number increased to slightly more than ten articles per year  The last numbers available for 2016 indicates this has increased to sixty articles per year 
The ability to mechanically harvest the sentiment from texts using NLP has shed light on conflicting theories of financial economics  Historically, there has been two differing views on whether disagreement among market participants induces more trades  The “non-trade theorem” BIBREF1 states that assuming all market participants have common knowledge about a market event, the level of disagreement among the participants does not increase the number of trades but only leads to a revision of the market quotes  In contrast, the theoretically framework proposed in BIBREF2 advocates that disagreement among market participants increases trading volume  Using textual data from Yahoo and RagingBull com message boards to measure the dispersion of opinions (positive or negative) among traders, it was shown in BIBREF3 that disagreement among users' messages helps to predict subsequent trading volume and volatility  Similar relation between disagreement and increased trading volume was found in BIBREF4 using Twitter posts  Additionally, textual analysis is adding to the theories of medium-term/long-term momentum/reversal in stock markets BIBREF5   The unified Hong and Stein model BIBREF6 on stock's momentum/reversal proposes that investors underreact to news, causing slow price drifts, and overreact to price shocks not accompanied by news, hence inducing reversals  This theoretical predicated behaviour between price and news was systematically estimated and supported in BIBREF7 , BIBREF8 using financial media headlines and in BIBREF9 using the Consumer Confidence Index® published by The Conference Board BIBREF10   Similarly, BIBREF11 uses the Harvard IV-4 sentiment lexicon to count the occurrence of words with positive and negative connotation of the Wall Street Journal showing that negative sentiment is a good predictor of price returns and trading volumes 
Accurate models for forecasting both price returns and volatility are equally important in the financial domain  Volatility measures how wildly the asset is expected to oscillate in a given time period and is related to the second moment of the price return distribution  In general terms, forecasting price returns is relevant to take speculative positions  The volatility, on the other hand, measures the risk of these positions  On a daily basis, financial institutions need to assess the short-term risk of their portfolios  Measuring the risk is essential in many aspects  It is imperative for regulatory capital disclosures required by banking supervision bodies  Moreover, it is useful to dynamically adjust position sizing accordingly to market conditions, thus, maintaining the risk within reasonable levels 
Although, it is crucial to predict the short-term volatility from the financial markets application perspective, much of the current NLP research on volatility forecasting focus on the volatility prediction for very long-term horizons (see BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 )  Predominately, these works are built on extensions of the bag-of-words representation that has the main drawback of not capturing word order  Financial forecasting, however, requires the ability to capture semantics that is dependent upon word order  For example, the headline “Qualcomm sues Apple for contract breach” and “Apple sues Qualcomm for contract breach” trigger different responses for each stock and for the market aggregated index, however, they share the same bag-of-words representation  Additionally, these works use features from a pretrained sentiment analyis model to train the financial forecasting model  A key limitation of this process is that it requires a labelled sentiment dataset  Additionally, the error propagation is not end-to-end  In this work, we fill in the gaps of volatility prediction research in the following manner:
Related work
Previous work in BIBREF12 incorporates sections of the “Form 10-K” to predict the volatility twelve months after the report is released  They train a Support Vector Regression model on top of sparse representation (bag-of-words) with standard term weighting (e g  Term-Frequency)  This work was extended in BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 by employing the Loughran-McDonald Sentiment Word Lists BIBREF20 , which contain three lists where words are grouped by their sentiments (positive, negative and neutral)  In all these works, the textual representation is engineered using the following steps: 1) For each sentiment group, the list is expanded by retrieving 20 most similar words for each word using Word2Vec word embeddings BIBREF21   2) Finally, each 10-K document is represented using the expanded lists of words  The weight of each word in this sparse representation is defined using Information Retrieval (IR) methods such as term-frequency (tf) and term-frequency with inverted document frequency (tfidf)  Particularly, BIBREF16 shows that results can be improved using enhanced IR methods and projecting each sparse feature into a dense space using Principal Component Analysis (PCA) 
The works described above ( BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 ) target long-horizon volatility predictions (one year or quarterly BIBREF16 )  In particular, BIBREF16 and BIBREF15 uses market data (price) features along with the textual representation of the 10-K reports  These existing works that employ multi-modal learning BIBREF22 are based on a late fusion approach  For example, stacking ensembles to take into account the price and text predictions BIBREF16   In contrast, our end-to-end trained model can learn the joint distribution of both price and text 
Predicting the price direction rather than the volatility was the focus in BIBREF23   They extracted sentiment words from Twitter posts to build a time series of collective Profile of Mood States (POMS)  Their results show that collective mood accurately predicts the direction of Down Jones stock index (86 7% accuracy)  In BIBREF24 handcrafted text representations including term count, noun-phrase tags and extracted named entities are employed for predicting stock market direction using Support Vector Machine (SVM)  An extension of Latent Dirichlet Allocation (LDA) is proposed in BIBREF25 to learn a joint latent space of topics and sentiments 
Our deep learning models bear a close resemblance to works focused on directional price forecasting BIBREF26 , BIBREF27   In BIBREF26 , headline news are processed using Stanford OpenIE to generate triples that are fed into a Neural Tensor Network to create the final headline representation  In BIBREF27 , a character-level embedding is pre-trained in an unsupervised manner  The character embedding is used as input to a sequence model to learn the headline representation  Particularly, both works average all headline representations in a given day, rather than attempting to weight the most relevant ones  In this work, we propose a neural attention mechanism to capture the News Relevance and provide experimental evidence that it is a key component of the end-to-end learning process  Our attention extends the previous deep learning methods from BIBREF26 , BIBREF27  
Despite the fact that end-to-end deep learning models have attained state-of-the-art performance, the large number of parameters make them prone to overfitting  Additionally, end-to-end models are trained from scratch requiring large datasets and computational resources  Transfer learning (TL) alleviates this problem by adapting representations learnt from a different and potentially weakly related source domain to the new target domain  For example, in computer vision tasks the convolutional features learnt from ImageNet BIBREF28 dataset (source domain) have been successfully transferred to multiple domain target tasks with much smaller datasets such as object classification and scene recognition BIBREF29   In this work, we consider TL in our experiments for two main reasons  First, it address the question whether our proposed dataset is suitable for end-to-end training since the performance of the transferred representations can be compared with end-to-end learning  Second, it is still to be investigated which dataset transfers better to the forecasting problem  Recently, the NLP community has focused on universal representations of sentences BIBREF17 , BIBREF19 , which are dense representations that carry the meaning of a full sentence  BIBREF17 found that transferring the sentence representation trained on the Stanford Natural Language Inference (SNLI) BIBREF30 dataset achieves state-of-the-art sentence representations to multiple NLP tasks (e g  sentiment analysis, question-type and opinion polarity)  Following BIBREF17 , in this work, we investigate the suitability of SNLI and Reuters RCV1 BIBREF31 datasets to transfer learning to the volatility forecasting task  To the best of our knowledge, the hierarchical attention mechanism at headline level, proposed in our work, has not being applied to volatility prediction so far; neither has been investigated the ability to transfer sentence encoders from source datasets to the target forecasting problem (Transfer Learning) 
Our dataset
Our corpus covers a broad range of news including news around earnings dates and complements the 10-K reports content  As an illustration, the headlines “Walmart warns that strong U S  dollar will cost $15B in sales” and “Procter & Gamble Co raises FY organic sales growth forecast after sales beat” describe the company financial conditions and performance from the management point of view – these are also typical content present in Section 7 of the 10-K reports 
In this section, we describe the steps involved in compiling our dataset of financial news at stock level, which comprises a broad range of business sectors 
Sectors and stocks
The first step in compiling our corpus was to choose the constituents stocks  Our goal was to consider stocks in a broad range of sectors, aiming a diversified financial domain corpus  We found that Exchange Traded Funds (ETF) provide a mechanical way to aggregate the most relevant stocks in a given industry/sector  An ETF is a fund that owns assets, e g  stock shares or currencies, but, unlike mutual funds are traded in stock exchanges  These ETFs are extremely liquid and track different investment themes  We decided to use SPDR Setcor Funds constituents stocks in our work since the company is the largest provider of sector funds in the United States  We included in our analysis the top 5 (five) sector ETFs by financial trading volume (as in Jan/2018)  Among the most traded sectors we also filtered out the sectors that were similar to each other  For example, the Consumer Staples and Consumer Discretionary sectors are both part of the parent Consumer category  For each of the top 5 sectors we selected the top 10 holdings, which are deemed the most relevant stocks  tbl:stockuniverse, details our dataset sectors and its respective stocks 
Stock specific data
We assume that an individual stock news as the one that explicitly mention the stock name or any of its surface forms in the headline  As an illustration, in order to collect all news for the stock code PG, Procter & Gamble company name, we search all the headlines with any of these words: Procter&Gamble OR Procter and Gamble OR P&G  In this example, the first word is just the company name and the remaining words are the company surface forms 
We automatically derived the surface forms for each stock by starting with a seed of surface forms extracted from the DBpedia Knowledge Base (KB)  We then applied the following procedure:
Relate each company name with the KB entity unique identifier 
Retrieve all values of the wikiPageRedirects property  The property holds the names of different pages that points to the same entity/company name  This step sets the initial seed of surface forms 
Manually, filter out some noisy property values  For instance, from the Procter & Glamble entity page we were able to automatically extract dbr:Procter_and_gamble and dbr:P_&_G, but had to manually exclude the noisy associations dbr:Female_pads and dbr:California_Natural 
The result of the steps above is a dictionary of surface forms $wd_{sc}$  
Stock headlines
Our corpus is built at stock code level by collecting headlines from the Reuters Archive  This archive groups the headlines by date, starting from 1 January 2007  Each headline is a html link (<a href> tag) to the full body of the news, where the anchor text is the headline content followed by the release time  For example, the page dated 16 Dec 2016 has the headline “Procter & Gamble appoints Nelson Peltz to board 5:26PM UTC” 
For each of the 50 stocks (5 sectors times 10 stocks per sector) selected using the criteria described in sub:corpussecstock, we retrieved all the headlines from the Reuters Archive raging from 01/01/2007 to 30/12/2017  This process takes the following steps:
For a given stock code ( $sc$ ) retrieve all surface forms $wd_{sc}$  
For each day, store only the headlines content matching any word in $wd_{sc}$   For each stored headline we also store the time and timezone 
Convert the news date and time to Eastern Daylight Time (EDT) 
Categorize the news release time  We consider the following category set: {before market, during market , after market, holidays, weekends}  during market contains news between 9:30AM and 4:00PM  before market before 9:30AM and after market after 4:00PM 
The time categories prevents any misalignment between text and stock price data  Moreover, it prevents data leakage and, consequently, unrealistic predictive model performance  In general, news released after 4:00PM EDT can drastically change market expectations and the returns calculated using close to close prices as in the GARCH(1,1) model (see eq:closingreturn)  Following BIBREF3 , to deal with news misalignment, news issued after 4:00PM (after market) are grouped with the pre-market (before market) on the following trading day 
tbl:stocktimecat shows the distribution of news per sector for each time category  We can see a high concentration of news released before the market opens (55% on average)  In contrast, using a corpus compiled from message boards, a large occurrence of news during market hours was found BIBREF3   This behaviour indicating day traders' activity  Our corpus comprise financial news agency headlines, a content more focused on corporate events (e g  lawsuits, merges & acquisitions, research & development) and on economic news (see tbl:stockheadlinesexmaples for a sample of our dataset)  These headlines are mostly factual  On the other hand, user-generated content such as Twitter and message boards (as in BIBREF3 , BIBREF4 ) tends to be more subjective 
U S  macroeconomic indicators such as Retail Sales, Jobless Claims and GDP are mostly released around 8:30AM (one hour before the market opens)  These numbers are key drivers of market activity and, as such, have a high media coverage  Specific sections of these economic reports impact several stocks and sectors  Another factor that contribute to the high activity of news outside regular trading hours are company earnings reports  These are rarely released during trading hours  Finally, before the market opens news agencies provide a summary of the international markets developments, e g  the key facts during the Asian and Australian trading hours  All these factors contribute to the high concentration of pre-market news 
Background
We start this section by reviewing the GARCH(1,1) model, which is a strong benchmark used to evaluate our neural model  We then review the source datasets proposed in the literature that were trained independently and transfered to our volatility prediction model  Finally, we review the general architectures of sequence modelling and attention mechanisms 
GARCH model
Financial institutions use the concept of “Value at risk” to measure the expected volatility of their portfolios  The widespread econometric model for volatility forecasting is the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) BIBREF32 , BIBREF33   Previous research shows that the GARCH(1,1) model is hard to beat  For example, BIBREF34 compared GARCH(1,1) with 330 different econometric volatility models showing that they are not significantly better than GARCH(1,1)  Let $p_t$ be the price of an stock at the end of a trading period with closing returns $r_t$ given by
$$r_t = \frac{p_t}{p_{t-1}} - 1 $$   (Eq  29)
The GARCH process explicitly models the time-varying volatility of asset returns  In the GARCH(1,1) specification the returns series $r_t$ follow the process:
$$r_t &= \mu + \epsilon _t  \\ \epsilon _t &= \sigma _t z_t  \\ \sigma ^2_t &= a_0 + a_1 \epsilon _{t-1}^2 + b_1 \sigma _{t-1}^2$$   (Eq  30)
where $\mu $ is a constant (return drift) and $z_t$ is a sequence of i i d  random variables with mean zero and unit variance  It is worth noting that although the conditional mean return described in eq:garchcondmean has a constant value, the conditional volatility $\sigma _t$ is time-dependent and modeled by eq:att 
The one-step ahead expected volatility forecast can be computed directly from eq:garchcondvariance and is given by
$$E_T[\sigma _{T+1}^2] = a_0 + a_1 E_T[\epsilon ^2] + b_1 E_T[\sigma _{T}^2] $$   (Eq  32)
In general, the $t^{\prime }$ -steps ahead expected volatility $E_T[\sigma _{T+t^{\prime }}^2]$ can be easily expressed in terms of the previous step expected volatility  It is easy to prove by induction that the forecast for any horizon can be represented in terms of the one-step ahead forecast and is given by
$$E_T[\sigma _{T+t^{\prime }}^2] - \sigma _u^2 = (a_1 + b_1)^{(t^{\prime } -1)} \left(E_T[\sigma _{T+1}^2] - \sigma _u^2\right)$$   (Eq  33)
where $\sigma _u$ is the unconditional volatility:
$$\sigma _u = \sqrt{a_0 / (1 - a_1 - b_1)} $$   (Eq  34)
From the equation above we can see that for long horizons, i e  $t^\prime \rightarrow \infty $ , the volatility forecast in eq:forecastrecursive converges to the unconditional volatility in eq:unvar 
All the works reviewed in sec:introduction ( BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 ) consider GARCH(1,1) benchmark  However, given the long horizon of their predictions (e g  quarterly or annual), the models are evaluated using the unconditional volatility $\sigma _u$ in eq:unvar  In this work, we focus on the short-term volatility prediction and use the GARCH(1,1) one-day ahead conditional volatility prediction in eq:forecastoneperiod to evaluate our models 
Let $\sigma _{t+1}$ denote the ex-post “true” daily volatility at a given time $t$   The performance on a set with $N$ daily samples can be evaluated using the standard Mean Squared Error ( $MSE$ ) and Mean Absolute Error ( $MAE$ )
$$MSE &= \frac{1}{N} \sum _{t=1}^{N} \left( E_t[\sigma _{t+1}] - \sigma _{t+1}\right)^2  \\ MAE &= \frac{1}{N} \sum _{t=1}^{N}\left|E_t[\sigma _{t+1}] - \sigma _{t+1} \right|$$   (Eq  36)
Additionally, following BIBREF35 , the models are also evaluated using the coefficient of determination $R^2$ of the regression
$$\sigma _{t+1} = a + b E_t[\sigma _{t+1}] + e_t$$   (Eq  37)
where
$$R^2 = 1 - \frac{\sum _{t=1}^{N}e^{2}_{t}}{\sum _{t=1}^{N}\left(E_t[\sigma _{t+1}] - \frac{1}{N} \sum _{t=1}^{N}E_t[\sigma _{t+1}]\right)^{2}}$$   (Eq  38)
One of the challenges in evaluating GARCH models is the fact that the ex-post volatility $\sigma _{t+1}$ is not directly observed  Apparently, the squared daily returns $r_{t+1}^{2}$ in eq:closingreturn could stand as a good proxy for the ex-post volatility  However, the squared returns yield very noisy measurements  This is a direct consequence of the term $z^t$ that connects the squared return to the latent volatility factor in eq:garchwhitenoise  The use of intraday prices to estimate the ex-post daily volayility was first proposed in BIBREF35   They argue that volatility estimators using intraday prices is the proper way to evaluate the GARCH(1,1) model, as opposed to squared daily returns  For example, considering the Deutsche Mark the GARCH(1,1) model $R^2$ improves from $0 047$ (squared returns) to $0 33$ (intraday returns) BIBREF35  
It is clear from the previous section that any volatility model evaluation using the noisy squared returns as the ex-post volatility proxy will lead to very poor performance  Therefore, high-frequency intraday data is fundamental to short-term volatility performance evaluation  However, intraday data is difficult to acquire and costly  Fortunately, there are statistically efficient daily volatility estimators that only depend on the open, high, low and close prices  These price “ranges” are widely available  In this section, we discuss these estimators 
Let $O_t$ , $H_t$ , $L_t$ , $C_t$ be the open, high, low and close prices of an asset in a given day $t$   Assuming that the daily price follows a geometric Brownian motion with zero drift and constant daily volatility $\sigma $ , Parkinson (1980) derived the first daily volatility estimator
$$\widehat{\sigma _{PK,t}^2} = \frac{\ln \left(\frac{H_t}{L_t}\right)^2}{4\ln (2)} $$   (Eq  41)
which represents the daily volatility in terms of its price range  Hence, it contains information about the price path  Given this property, it is expected that $\sigma _{PK}$ is less noisy than the volatility calculated using squared returns  The Parkinson's volatility estimator was extended by Garman-Klass (1980) which incorporates additional information about the opening ( $O_t$ ) and closing ( $C_t$ ) prices and is defined as
$$\widehat{\sigma _{GK,t}^{2}} = \frac{1}{2} \ln \left(\frac{H_t}{L_t}\right)^2 - (2\ln (2) - 1) \ln \left(\frac{C_t}{O_t}\right)^2 $$   (Eq  42)
The relative noisy of different estimators $\hat{\sigma }$ can be measured in terms of its relative efficiency to the daily volatility $\sigma $ and is defined as
$$e\left(\widehat{\sigma ^{2}}, \sigma ^2\right) \equiv \frac{Var[\sigma ^2]}{Var[\widehat{\sigma ^{2}}]}$$   (Eq  43)
where $Var[\cdot ]$ is the variance operator  It follows directly from eq:garchwhitenoise that the squared return has efficiency 1 and therefore, very noisy  BIBREF36 reports Parkinson ( $\widehat{\sigma _{PK,t}^2}$ ) volatility estimator has 4 9 relative efficiency and Garman-Klass ( $\widehat{\sigma _{GK,t}^2}$ ) 7 4  Additionally, all the described estimators are unbiased 
Many alternative estimators to daily volatility have been proposed in the literature  However, experiments in BIBREF36 rate the Garman-Klass volatility estimator as the best volatility estimator based only on open, high, low and close prices  In this work, we train our models to predict the state-of-the-art Garman-Klass estimator  Moreover, we evaluate our models and GARCH(1,1) using the metrics described in sub:evalution, but with the appropriate volatility proxies, i e  Parkinson and Garman-Klass estimators 
Transfer Learning from other source domains
Vector representations of words, also known as Word embeddings BIBREF21 , BIBREF37 , that represent a word as a dense vector has become the standard building blocks of almost all NLP tasks  These embeddings are trained on large unlabeled corpus and are able to capture context and similarity among words 
Some attempts have been made to learn vector representations of a full sentence, rather than only a single word, using unsupervised approaches similar in nature to word embeddings  Recently, BIBREF17 showed state-of-the-art performance when a sentence encoder is trained end-to-end on a supervised source task and transferred to other target tasks  Inspired by this work, we investigate the performance of sentence encoders trained on the Text categorization and Natural Language Inference (NLI) tasks and use these encoders in our main short-term volatility prediction task 
A generic sentence encoder $S_e$ receives the sentence words as input and returns a vector representing the sentence  This can be expressed as a mapping
$$S_e \colon \mathbb {R}^{T^{S} \times d_w} \rightarrow \mathbb {R}^{d_S}$$   (Eq  45)
from a variable size sequence of words to a sentence vector $S$ of fixed-size $d_S$ , where $T^{S}$ is the sentence number of words and $d_w$ is the pre-trained word embedding dimension 
In the following sections, we describe the datasets and architectures to train the sentence encoders of the auxiliary transfer learning tasks 
The Reuters Corpus Volume I (RCV1) is corpus containing 806,791 news articles in the English language collected from 20/08/1996 to 19/08/1997 BIBREF31   The topic of each news was human-annotated using a hierarchical structure  At the top of the hierarchy, lies the coarse-grained categories: CCAT (Corporate), ECAT (Economics), GCAT (Government), and MCAT (Markets)  A news article can be assigned to more than one category meaning that the text categorization task is mutilabel  Each news is stored in a separate XML file  lst:rcv1xmlexample shows the typical structure of an article 
<?xml version="1 0" encoding="iso-8859-1" ?>
<newsitem itemid="6159" id="root" date="1996-08-21" xml:lang="en">
<headline>Colombia raises internal coffee price </headline>
<dateline>BOGOTA 1996-08-21</dateline>
<copyright>(c) Reuters Limited 1996</copyright>
<metadata>
<codes class="bip:topics:1 0">
<code code="C13">
<editdetail attribution="Reuters BIP Coding Group" action="confirmed" date="1996-08-21"/>
</code>
<code code="C31">
<editdetail attribution="Reuters BIP Coding Group" action="confirmed" date="1996-08-21"/>
</code>
<code code="CCAT">
<editdetail attribution="Reuters BIP Coding Group" action="confirmed" date="1996-08-21"/>
</code>
<code code="M14">
<editdetail attribution="Reuters BIP Coding Group" action="confirmed" date="1996-08-21"/>
</code>
<code code="M141">
<editdetail attribution="Reuters BIP Coding Group" action="confirmed" date="1996-08-21"/>
</code>
<code code="MCAT">
<editdetail attribution="Reuters BIP Coding Group" action="confirmed" date="1996-08-21"/>
</code>
</codes>
</metadata>
</newsitem>
The RCV1 dataset is not released with a standard train, validation, test split  In this work, we separated 15% of samples as a test set for evaluation purposes  The remaining samples were further split leaving 70% and 15% for training and validation, respectively 
Regarding the categories distribution, we found that, from the original 126 categories, 23 categories were never assigned to any news; therefore, were disregarded  From the 103 classes left we found a high imbalance among the labels with a large number of underrepresented categories having less than 12 samples  The very low number of samples for these minority classes brings a great challenge to discriminate the very fine-grained categories  Aiming to alleviate this problem, we grouped into a same class all categories below the second hierarchical level  For example, given the root node CCAT (Corporate) we grouped C151 (ACCOUNTS/EARNINGS), C1511 (ANNUAL RESULTS) and C152 (COMMENT/FORECASTS) into the direct child node C15 (PERFORMANCE)  Using this procedure the original 103 categories where reduced to 55  One of the benefits of this procedure was that the less represented classes end up having around thousand samples compared with only 12 samples in the original dataset 
fig:rcv1arch, shows the architecture for the end-to-end text categorization task  On the bottom of the architecture $S_e$ receives word embeddings and outputs a sentence vector $S$   The $S$ vector pass through a fully connected (FC) layer with sigmoid activation function that outputs a vector $\hat{y} \in \mathbb {R}^{55}$ with each element $\hat{y}_j \in [0,1]$  
The architecture described above is trained under the assumption that each category is independent but not mutually exclusive since a sample can have more than one category assigned (multilabel classification)  The loss per sample is the average log loss across all labels:
$$\mathcal {L}(\hat{y}, y) = - \sum _{i=1}^{55}\left( y_i \log (\hat{y}_i) + (1-y_{i}) \log (1-\hat{y}_{i}) \right)$$   (Eq  48)
where the index $i$ runs over the elements of the predicted and true vectors 
Given the high categories imbalance, during the training we monitor the $F_1$ metric of the validation set and choose the model with the highest value 
Stanford Natural Language Inference (SNLI) dataset BIBREF30 consist of 570,000 pairs of sentences  Each pair has a premise and a hypothesis, manually labeled with one of the three labels: entailment, contradiction, or neutral  The SNLI has many desired properties  The labels are equally balanced, as opposed to the RCV1 dataset  Additionally, language inference is a complex task that requires a deeper understanding of the sentence meaning making this dataset suitable for learning supervised sentence encoders that generalize well to other tasks BIBREF17   tbl:snliexmaples, shows examples of SNLI dataset sentence pairs and its respective labels 
In order to learn sentence encoders that can be transfered to other tasks unambiguously, we consider a neural network architecture for the sentence encoder with shared parameters between the premise and hypothesis pairs as in BIBREF17  
fig:snliarch, describes the neural network architecture  After each premise and hypothesis is encoded into $S_p$ and $S_h$ , respectively, we have a fusion layer  This layer has no trainable weights and just concatenate each sentence embedding  Following BIBREF17 , we add two more matching methods: the absolute difference $\vert S_p - S_h \vert $ and the element-wise $S_p \odot S_h$   Finally, in order to learn the pair representation, $S_ph$ is feed into and FC layer with rectified linear unit (ReLU) activation function, which is expressed as $f(x) = \log (1 + e^x)$   The last softmax layer outputs the probability of each class 
Finally, the NLI classifier weights are optimized in order to minimize the categorical log loss per sample
$$\mathcal {L}(\hat{y}, y) = - \sum _{j=1}^{3}y_i \log (\hat{y}_i)$$   (Eq  52)
During the training, we monitor the validation set accuracy and choose the model with the highest metric value 
Sequence Models
We start this section by reviewing the Recurrent Neural Network (RNN) architecture and its application to encode a sequence of words 
RNN's are capable of handling variable-length sequences, this being a direct consequence of its recurrent cell, which shares the same parameters across all sequence elements  In this work, we adopt the Long Short-Term Memory (LSTM) cell BIBREF38 with forget gates $f_t$ BIBREF39   The LSTM cell is endowed with a memory state that can learn representations that depend on the order of the words in a sentence  This makes LSTM more fit to find relations that could not be captured using standard bag-of-words representations 
Let $x_1, x_2, \cdots , x_T$ be a series of observations of length $T$ , where $x_t \in \mathbb {R}^{d_w}$   In general terms, the LSTM cell receives a previous hidden state $h_{t-1}$ that is combined with the current observation $x_t$ and a memory state $C_t$ to output a new hidden state $h_t$   This internal memory state $C_{t}$ is updated depending on its previous state and three modulating gates: input, forget, and output  Formally, for each step $t$ the updating process goes as follows (see fig:lstmcell for a high level schematic view): First, we calculate the input $i_t$ , forget $T$0 , and output $T$1 gates:
$$i_t &= \sigma _s\left(W_i x_t + U_i h_{t-1} + b_i\right) \\ f_t &= \sigma _s\left(W_f x_t + U_f h_{t-1} + b_f\right) \\ o_t &= \sigma _s\left(W_o x_t + U_o h_{t-1} + b_o\right)$$   (Eq  54)
where $\sigma _s$ is the sigmoid activation

ground_truth: English

English 

English

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 traditional linguistics features did they use?
Context 2:  Figure FIGREF14 presents scanpaths of three participants for the sarcastic sentence S1 and the non-sarcastic sentence S2  The x-axis of the graph represents the sequence of words a reader reads, and the y-axis represents a temporal sequence in milliseconds 
Consider a sarcastic text containing incongruous phrases A and B  Our qualitative scanpath-analysis reveals that scanpaths with respect to sarcasm processing have two typical characteristics  Often, a long regression - a saccade that goes to a previously visited segment - is observed when a reader starts reading B after skimming through A  In a few cases, the fixation duration on A and B are significantly higher than the average fixation duration per word  In sentence S1, we see long and multiple regressions from the two incongruous phrases “misconception” and “cherish”, and a few instances where phrases “always cherish” and “original misconception” are fixated longer than usual  Such eye-movement behaviors are not seen for S2 
Though sarcasm induces distinctive scanpaths like the ones depicted in Figure FIGREF14 in the observed examples, presence of such patterns is not sufficient to guarantee sarcasm; such patterns may also possibly arise from literal texts  We believe that a combination of linguistic features, readability of text and features derived from scanpaths would help discriminative machine learning models learn sarcasm better 
Features for Sarcasm Detection
We describe the features used for sarcasm detection in Table   The features enlisted under lexical,implicit incongruity and explicit incongruity are borrowed from various literature (predominantly from joshi2015harnessing)  These features are essential to separate sarcasm from other forms semantic incongruity in text (for example ambiguity arising from semantic ambiguity or from metaphors)  Two additional textual features viz  readability and word count of the text are also taken under consideration  These features are used to reduce the effect of text hardness and text length on the eye-movement patterns 
Simple Gaze Based Features
Readers' eye-movement behavior, characterized by fixations, forward saccades, skips and regressions, can be directly quantified by simple statistical aggregation (i e , either computing features for individual participants and then averaging or performing a multi-instance based learning as explained in section SECREF6 )  Since these eye-movement attributes relate to the cognitive process in reading BIBREF17 , we consider these as features in our model  Some of these features have been reported by sarcasmunderstandability for modeling sarcasm understandability of readers  However, as far as we know, these features are being introduced in NLP tasks like textual sarcasm detection for the first time  The values of these features are believed to increase with the increase in the degree of surprisal caused by incongruity in text (except skip count, which will decrease) 
Complex Gaze Based Features
For these features, we rely on a graph structure, namely “saliency graphs", derived from eye-gaze information and word sequences in the text 
For each reader and each sentence, we construct a “saliency graph”, representing the reader's attention characteristics  A saliency graph for a sentence INLINEFORM0 for a reader INLINEFORM1 , represented as INLINEFORM2 , is a graph with vertices ( INLINEFORM3 ) and edges ( INLINEFORM4 ) where each vertex INLINEFORM5 corresponds to a word in INLINEFORM6 (may not be unique) and there exists an edge INLINEFORM7 between vertices INLINEFORM8 and INLINEFORM9 if R performs at least one saccade between the words corresponding to INLINEFORM10 and INLINEFORM11  
Figure FIGREF15 shows an example of a saliency graph A saliency graph may be weighted, but not necessarily connected, for a given text (as there may be words in the given text with no fixation on them)  The “complex” gaze features derived from saliency graphs are also motivated by the theory of incongruity  For instance, Edge Density of a saliency graph increases with the number of distinct saccades, which could arise from the complexity caused by presence of sarcasm  Similarly, the highest weighted degree of a graph is expected to be higher, if the node corresponds to a phrase, incongruous to some other phrase in the text 
The Sarcasm Classifier
We interpret sarcasm detection as a binary classification problem  The training data constitutes 994 examples created using our eye-movement database for sarcasm detection  To check the effectiveness of our feature set, we observe the performance of multiple classification techniques on our dataset through a stratified 10-fold cross validation  We also compare the classification accuracy of our system and the best available systems proposed by riloff2013sarcasm and joshi2015harnessing on our dataset  Using Weka BIBREF18 and LibSVM BIBREF19 APIs, we implement the following classifiers:
Results
Table TABREF17 shows the classification results considering various feature combinations for different classifiers and other systems  These are:
Unigram (with principal components of unigram feature vectors),
Sarcasm (the feature-set reported by joshi2015harnessing subsuming unigram features and features from other reported systems)
Gaze (the simple and complex cognitive features we introduce, along with readability and word count features), and
Gaze+Sarcasm (the complete set of features) 
For all regular classifiers, the gaze features are averaged across participants and augmented with linguistic and sarcasm related features  For the MILR classifier, the gaze features derived from each participant are augmented with linguistic features and thus, a multi instance “bag” of features is formed for each sentence in the training data  This multi-instance dataset is given to an MILR classifier, which follows the standard multi instance assumption to derive class-labels for each bag 
For all the classifiers, our feature combination outperforms the baselines (considering only unigram features) as well as BIBREF3 , with the MILR classifier getting an F-score improvement of 3 7% and Kappa difference of 0 08  We also achieve an improvement of 2% over the baseline, using SVM classifier, when we employ our feature set  We also observe that the gaze features alone, also capture the differences between sarcasm and non-sarcasm classes with a high-precision but a low recall 
To see if the improvement obtained is statistically significant over the state-of-the art system with textual sarcasm features alone, we perform McNemar test  The output of the SVM classifier using only linguistic features used for sarcasm detection by joshi2015harnessing and the output of the MILR classifier with the complete set of features are compared, setting threshold INLINEFORM0   There was a significant difference in the classifier's accuracy with p(two-tailed) = 0 02 with an odds-ratio of 1 43, showing that the classification accuracy improvement is unlikely to be observed by chance in 95% confidence interval 
Considering Reading Time as a Cognitive Feature along with Sarcasm Features
One may argue that, considering simple measures of reading effort like “reading time” as cognitive feature instead of the expensive eye-tracking features for sarcasm detection may be a cost-effective solution  To examine this, we repeated our experiments with “reading time” considered as the only cognitive feature, augmented with the textual features  The F-scores of all the classifiers turn out to be close to that of the classifiers considering sarcasm feature alone and the difference in the improvement is not statistically significant ( INLINEFORM0 )  One the other hand, F-scores with gaze features are superior to the F-scores when reading time is considered as a cognitive feature 
How Effective are the Cognitive Features
We examine the effectiveness of cognitive features on the classification accuracy by varying the input training data size  To examine this, we create a stratified (keeping the class ratio constant) random train-test split of 80%:20%  We train our classifier with 100%, 90%, 80% and 70% of the training data with our whole feature set, and the feature combination from joshi2015harnessing  The goodness of our system is demonstrated by improvements in F-score and Kappa statistics, shown in Figure FIGREF22  
We further analyze the importance of features by ranking the features based on (a) Chi squared test, and (b) Information Gain test, using Weka's attribute selection module  Figure FIGREF23 shows the top 20 ranked features produced by both the tests  For both the cases, we observe 16 out of top 20 features to be gaze features  Further, in each of the cases, Average Fixation Duration per Word and Largest Regression Position are seen to be the two most significant features 
Example Cases
Table TABREF21 shows a few example cases from the experiment with stratified 80%-20% train-test split 
Example sentence 1 is sarcastic, and requires extra-linguistic knowledge (about poor living conditions at Manchester)  Hence, the sarcasm detector relying only on textual features is unable to detect the underlying incongruity  However, our system predicts the label successfully, possibly helped by the gaze features 
Similarly, for sentence 2, the false sense of presence of incongruity (due to phrases like “Helped me” and “Can't stop”) affects the system with only linguistic features  Our system, though, performs well in this case also 
Sentence 3 presents a false-negative case where it was hard for even humans to get the sarcasm  This is why our gaze features (and subsequently the complete set of features) account for erroneous prediction 
In sentence 4, gaze features alone false-indicate presence of incongruity, whereas the system predicts correctly when gaze and linguistic features are taken together 
From these examples, it can be inferred that, only gaze features would not have sufficed to rule out the possibility of detecting other forms of incongruity that do not result in sarcasm 
Error Analysis
Errors committed by our system arise from multiple factors, starting from limitations of the eye-tracker hardware to errors committed by linguistic tools and resources  Also, aggregating various eye-tracking parameters to extract the cognitive features may have caused information loss in the regular classification setting 
Conclusion
In the current work, we created a novel framework to detect sarcasm, that derives insights from human cognition, that manifests over eye movement patterns  We hypothesized that distinctive eye-movement patterns, associated with reading sarcastic text, enables improved detection of sarcasm  We augmented traditional linguistic features with cognitive features obtained from readers' eye-movement data in the form of simple gaze-based features and complex features derived from a graph structure  This extended feature-set improved the success rate of the sarcasm detector by 3 7%, over the best available system  Using cognitive features in an NLP Processing system like ours is the first proposal of its kind 
Our general approach may be useful in other NLP sub-areas like sentiment and emotion analysis, text summarization and question answering, where considering textual clues alone does not prove to be sufficient  We propose to augment this work in future by exploring deeper graph and gaze features  We also propose to develop models for the purpose of learning complex gaze feature representation, that accounts for the power of individual eye movement patterns along with the aggregated patterns of eye movements 
Acknowledgments
We thank the members of CFILT Lab, especially Jaya Jha and Meghna Singh, and the students of IIT Bombay for their help and support 

ground_truth: Unanswerable

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

Please answer the questions based on the text provided below. Which stock market sector achieved the best performance?
Context 3:  Second, a candidate memory state $\widetilde{C}_t$ is generated:
$$\widetilde{C}_t = \tanh \left(W_c x_t + U_c h_{t-1} + b_c\right)$$   (Eq  55)
Now we are in a position to set the final memory state $C_t$   Its value is modulated based on the input and forget gates of eq:inputforgetgates and is given by:
$$C_t = i_t \odot \widetilde{C}_t + f_t \odot C_{t-1}$$   (Eq  56)
Finally, based on the memory state and output gate of eq:inputforgetgates, we have the output hidden state
$$h_t = o_t \odot \tanh \left(C_t\right)$$   (Eq  57)
Regarding the trainable weights, let $n$ be the LSTM cell number of units  It follows that $W$ 's and $U$ 's matrices of the affine transformations have ${n \times d_w}$ and ${n \times n}$ dimensions, respectively  Its bias terms $b$ 's are vectors of size $n$   Consequently, the total number of parameters is $4 (n d_w + n^2 + n)$ and does not depend on the sequence number of time steps $T$  
We see that the LSTM networks are able to capture temporal dependencies in sequences of arbitrary length  One straightforward application is to model the Sentence encoder discussed in sec:transferlearning, which outputs a sentence vector representation using its words as input 
Given a sequence of words $\left\lbrace w_t\right\rbrace _{t=1}^{T}$ we aim to learn the words hidden state $\left\lbrace h_t\right\rbrace _{t=1}^{T}$ in a way that each word captures the influence of its past and future words  The Bidirectional LSTM (BiLSTM) proposed in BIBREF40 is an LSTM that “reads” a sentence, or any sequence in general, from the beginning to the end (forward) and the other way around (backward)  The new state $h_t$ is the concatenation
$$h_t = [\overrightarrow{h_t}, \overleftarrow{h_t}]$$   (Eq  59)
where
$$\overrightarrow{h_t} &= \text{LSTM}\left(w_1, \cdots , w_T\right) \\ \overleftarrow{h_t} &= \text{LSTM}\left(w_T, \cdots , w_1\right) \\$$   (Eq  60)
Because sentences have different lengths, we need to convert the $T$ concatenated hidden states of the BiLSTM into a fixed-length sentence representation  One straightforward operation is to apply any form of pooling  Attention mechanism is an alternative approach where the sentence is represented as an weighted average of hidden states where the weights are learnt end-to-end 
In the next sections we describe the sentence encoders using pooling and attention layers 
The max-pooling layer aims to extract the most salient word features all over the sentence  Formally, it outputs a sentence vector representation $S_{MP} \in \mathbb {R}^{2n}$ such that
$$S_{MP} = \max _{t=1}^{T} h_t$$   (Eq  62)
where $h_t$ is defined in eq:htconcat and the $\max $ operator is applied over the time steps dimension  fig:bilstmmaxpool illustrates the BiLSTM max-pooling (MP) sentence encoder 
The efficacy of the max-pooling layer was assessed in many NLP studies  BIBREF41 employed a max-pooling layer on top of word representations and argues that it performs better than mean pooling  Experimental results in BIBREF17 show that among three types of pooling (max, mean and last) the max-pooling provides the most universal sentence representations in terms of transferring performance to other tasks  Grounded on these studies, in this work, we choose the BiLSTM max-pooling as our pooling layer of choice 
Attention mechanisms were introduced in the deep learning literature to overcome some simplifications imposed by pooling operators  When we humans read a sentence, we are able to spot its most relevant parts in a given context and disregard information that is redundant or misleading  The attention model aims to mimic this behaviour 
Attention layers were proposed for different NLP tasks  For example, NLI, with cross-attention between premise and hypothesis, Question & Answering and Machine Translation (MT)  Specifically in the Machine Translation task, each word in the target sentence learns to attend the relevant words of the source sentence in order to generate the sentence translation 
A sentence encoder with attention (or self-attentive) BIBREF42 , BIBREF43 , BIBREF44 assigns different weights to the own words of the sentence; therefore, converting the hidden states into a single sentence vector representation 
Considering the word hidden vectors set $\lbrace h_1, \cdots , h_T\rbrace $ where $h_t \in \mathbb {R}^n$ , the attention mechanism is defined by the equations:
$$\tilde{h}_t &= \sigma \left(W h_t + b \right) \\ \alpha _{t} &= \frac{\exp ({v^{\intercal } \cdot \tilde{h}_t} )}{\sum _{t} \exp ({v \cdot \tilde{h}_t})} \\ S_{A_w} &= \sum _{t} \alpha _{t} h_t$$   (Eq  66)
where $W \in \mathbb {R}^{d_a \times n}$ , $b \in \mathbb {R}^{d_a \times 1}$ , and $v \in \mathbb {R}^{d_a \times 1}$ are trainable parameters 
We can see that the sentence representation $S_{A_w}$ is a weighted average of the hidden states  fig:bilstminneratt provides a schematic view of the BiLSTM attention, where we can account the attention described in eq:att as a two layer model with a dense layer ( $d_a$ units) followed by another dense that predicts $\alpha _t$ (single unit) 
Methodology
In this section, we first introduce our problem in a deep multimodal learning framework  We then present our neural architecture, which is able to address the problems of news relevance and novelty  Finally, we review the methods applied to learn commonalities between stocks (global features) 
Problem statement
Our problem is to predict the daily stock volatility  As discussed in subsub:rangevolestimators, the Gaman-Klass estimator $\widehat{\sigma _{GK,t}}$ in eq:volgk is a very efficient short-term volatility proxy, thus, it is adopted as our target variable 
Our goal is to learn a mapping between the next day volatility $\sigma _{t+1}$ and historical multimodal data available up to day $t$   To this aim, we use a sliding window approach with window size $T$   That is, for each stock $sc$ a sample on day $t$ is expressed as a sequence of historical prices $P^{sc}_t$ and corpus headlines $N^{sc}_t$   The price sequence is a vector of Daily Prices (DP) and expressed as
$$P^{sc}_t = \left[DP^{sc}_{t-T}, DP^{sc}_{t-T+1}, \cdots , DP^{sc}_t \right]$$   (Eq  69)
where $DP^{sc}_{t^{\prime }}$ is a vector of price features  In order to avoid task-specific feature engineering, the daily price features are expressed as the simple returns:
$$DP^{sc}_t = \left[ \frac{O^{sc}_{t}}{C^{sc}_{t-1}} - 1, \frac{H^{sc}_{t}}{C^{sc}_{t-1}} - 1, \frac{L^{sc}_{t}}{C^{sc}_{t-1}} - 1, \frac{C^{sc}_{t}}{C^{sc}_{t-1}} - 1 \right]$$   (Eq  70)
The sequence of historical corpus headlines $N^{sc}_t$ is expressed as
$$N^{sc}_t = \left[n^{sc}_{t-T}, n^{sc}_{t-T+1}, \cdots , n^{sc}_{t} \right]$$   (Eq  71)
where $n^{sc}_{t^{\prime }}$ is a set containing all headlines that influence the market on a given day $t^{\prime }$  
Aiming to align prices and news modes, we consider the explicit alignment method discussed in subsec:stockheadlines  That is, $n^{sc}_{t^{\prime }}$ contains all stock headlines before the market opens ( $\texttt {before market}_{t}$ ), during the trading hours
( $\texttt {during market}_{t}$ ), and previous day after-markets
( $\texttt {after market}_{t-1}$ ) 
As a text preprocessing step, we tokenize the headlines and convert each word to an integer that refers to its respective pre-trained word embedding  This process is described as follows: First, for all stocks of our corpus we tokenize each headline and extract the corpus vocabulary set $V$   We then build the embedding matrix $E_w \in \mathbb {R}^{\vert V \vert \times d_w}$ , where each row is a word embedding vector $d_w$ dimensions  Words that do not have a corresponding embedding, i e  out of vocabulary words, are skipped 
Finally, the input sample of the text mode is a tensor of integers with $T \times l_n \times l_s$ dimensions, where $l_n$ is the maximum number of news occurring in a given day and $l_s$ is the maximum length of a corpus sentence  Regarding the price mode, we have a $T \times 4$ tensor of floating numbers 
Global features and stock embedding
Given the price and news histories for each stock $sc$ we could directly learn one model per stock  However, this approach suffers from two main drawbacks  First, the market activity of one specific stock is expected to impact other stocks, which is a widely accepted pattern named “spillover effect”  Second, since our price data is sampled on a daily basis, we would train the stock model relying on a small number of samples  One possible solution to model the commonality among stocks would be feature enrichment  For example, when modeling a given stock $X$ we would enrich its news and price features by concatenating features from stock $Y$ and $Z$   Although the feature enrichment is able to model the effect of other stocks, it still would consider only one sample per day 
In this work, we propose a method that learns an global model 
The global model is implemented using the following methods:
Multi-Stock batch samples: Since our models are trained using Stochastic Gradient Descent, we propose at each mini-batch iteration to sample from a batch set containing any stock of our stocks universe  As a consequence, the mapping between volatility and multimodal data is now able to learn common explanatory factors among stocks  Moreover, adopting this approach increases the total number of training samples, which is now the sum of the number of samples per stock 
Stock Embedding: Utilizing the Multi-Stock batch samples above, we tackle the problem of modeling commonality among stocks  However, it is reasonable to assume that stocks have part of its dynamic driven by idiosyncratic factors  Nevertheless, we could aggregate stocks per sector or rely on any measure of similarity among stocks  In order to incorporate information specific to each stock, we propose to equip our model with a “stock embedding” mode that is learnt jointly with price and news modes  That is to say, we leave the task of distinguishing the specific dynamic of each stock to be learnt by the neural network  Specifically, this stock embedding is modeled using a discrete encoding as input, i e  $\mathcal {I}^{sc}_t$ is a vector with size equal to the number of stocks of the stocks universe and has element 1 for the i-th coordinate and 0 elsewhere, thus, indicating the stock of each sample 
Formally, we can express the one model per stock approach as the mapping
$$\begin{split} \sigma ^{sc}_{t+1} = f^{sc} ( DN^{sc}_{t-T}, DN^{sc}_{t-T+1}, \cdots , DN^{sc}_t ; \\ DP^{sc}_{t-T}, DP^{sc}_{t-T+1}, \cdots , DP^{sc}_t ) \end{split}$$   (Eq  75)
where $DN^{sc}_{t^{\prime }}$ is a fixed-vector representing all news released on a given day for the stock $sc$ and $DP^{sc}_{t^{\prime }}$ is defined in eq:pricemodevec 
The global model attempts to learn a single mapping $f$ that at each mini-batch iteration randomly aggregates samples across all the universe of stocks, rather than one mapping $f^{sc}$ per stock  The global model is expressed as
$$\begin{split} \sigma ^{sc}_{t+1} = f ( DN^{sc}_{t-T}, DN^{sc}_{t-T+1}, \cdots , DN^{sc}_t ; \\ DP^{sc}_{t-T}, DP^{sc}_{t-T+1}, \cdots , DP^{sc}_t ; \\ \mathcal {I}^{sc}_t) \end{split}$$   (Eq  77)
In the next section, we describe our hierarchical neural model and how the news, price and stock embedding are fused into a joint representation 
Our multimodal hierarchical network
In broad terms, our hierarchical neural architecture is described as follows  First, each headline released on a given day $t$ is encoded into a fixed-size vector $S_t$ using a sentence encoder  We then apply our daily New Relevance Attention (NRA) mechanism that attends each news based on its content and converts a variable size of news released on a given day into a single vector denoted by Daily News ( $DN$ )  We note that this representation take account of the overall effect of all news released on a given day  This process is illustrated in fig:DNencoder  We now are in a position to consider the temporal effect of the past $T$ days of market news and price features  fig:nntimeseriesarch illustrates the neural network architecture from the temporal sequence to the final volatility prediction  For each stock code $sc$ the temporal encoding for news is denoted by Market News $MN^{sc}_t$ and for the price by Market Price $MP^{sc}_t$ and are a function of the past $T$ Daily News representations ${\lbrace DN^{sc}_{t-T}, \cdots , DN^{sc}_t \rbrace }$ (Text mode) and Daily Prices features $S_t$0 (Price mode), where each Daily Price $S_t$1 feature is given by eq:pricemodevec and the $S_t$2 representation is calculated using Daily New Relevance Attention  After the temporal effects of $S_t$3 past days of market activity were already encoded into the Market News $S_t$4 and Market Price $S_t$5 , we concatenate feature-wise $S_t$6 , $S_t$7 and the Stock embedding $S_t$8   The stock embedding $S_t$9 represents the stock code of the sample on a given day $t$   Finally, we have a Fully Connected (FC) layer that learns the Joint Representation of all modes  This fixed-sized joint representation is fed into a FC layer with linear activation that predicts the next day volatility $\hat{\sigma }_{t+1}$  
Below, we detail, for each mode separately, the layers of our hierarchical model 
– Text mode
Word Embedding Retrieval
Standard embedding layer with no trainable parameters  It receives a vector of word indices as input and returns a matrix of word embeddings 
News Encoder
This layer encodes all news on a given day and outputs a set news embeddings $\lbrace S^{1}_t, \cdots , S^{l_n}_t \rbrace $   Each encoded sentence has dimension $d_S$ , which is a hyperparameter of our model  This layer constitutes a key component of our neural architectures and, as such, we evaluate our models considering sentence encoders trained end-to-end, using the BiLSTM attention (subsec:bilstminneratt) and BiLSTM max-pooling (subsec:bilstmmaxpool) architectures, and also transferred from the RCV1 and SNLI as fixed features 
Daily news relevance attention
Our proposed news relevance attention mechanism for all news released on a given day  The attention mechanism is introduced to tackle information overload  It was designed to “filter out” redundant or misleading news and focus on the relevant ones based solely on the news content  Formally, the layer outputs a Daily News (DN) embedding $DN^{sc}_t = \sum _{i=1}^{l_n} \beta _i S^{sc^{i}}_t$ , which is a linear combination of all encoded news on a given day $t$   This news-level attention uses the same equations as in eq:att, but with trainable weights $\lbrace W_{R}, b_{R}, v_{R}\rbrace $ , i e  the weights are segregated from the sentence encoder  fig:DNencoder, illustrates our relevance attention  Note that this layer was deliberately developed to be invariant to headlines permutation, as is the case with the linear combination formula above  The reason is that our price data is sampled daily and, as a consequence, we are not able to discriminate the market reaction for each intraday news 
News Temporal Context
Sequence layer with daily news embeddings $DN^{sc}_t$ as time steps  This layer aims to learn the temporal context of news, i e  the relationship between the news at day $t$ and the $T$ past days  It receives as input a chronologically ordered sequence of $T$ past Daily News embeddings ${\lbrace DN^{sc}_{t-T}, \cdots , DN^{sc}_t \rbrace }$ and outputs the news mode encoding Market News $MN^{sc}_t \in d_{MN}$   The sequence with $T$ time steps is encoded using a BiLSTM attention  The layer was designed to capture the temporal order that news are released and the current news novelty  i e  news that were repeated in the past can be “forgotten” based on the modulating gates of the LSTM network 
– Price mode
Price Encoder
Sequence layer analogous to News Temporal Context, but for the price mode  The input is the ordered sequence Daily Prices ${\lbrace DP^{sc}_{t-T}, \cdots , DP^{sc}_t \rbrace }$ of size $T$ , where each element the price feature defined in eq:pricemodevec  Particularly, the architecture consists of two stacked LSTM's  The first one outputs for each price feature time step a hidden vector that takes the temporal context into account  Then these hidden vectors are again passed to a second independent LSTM  The layer outputs the price mode encoding Market Price $MP^{sc}_t \in d_{MP}$   This encoding is the last hidden vector of the second LSTM Market 
– Stock embedding
Stock Encoder
Stock dense representation  The layer receives the discrete encoding $\mathcal {I}^{sc}_t$ indicating the sample stock code pass through a FC layer and outputs a stock embedding $E_{sc}$  
– Joint Representation
Merging
Feature-wise News, Price, and Stock modes concatenation  No trainable parameters 
Joint Representation Encoder
FC layer of size $d_{JR}$  
Multimodal learning with missing modes
During the training we feed into our neural model the price, news, and stock indicator data  The price and stock indicator modes data occur in all days  However, at the individual stock level we can have days that the company is not covered by the media  This feature imposes challenges to our multimodal training since neural networks are not able to handle missing modes without special intervention  A straightforward solution would be to consider only days with news released, disregarding the remaining samples  However, this approach has two main drawbacks  First, the “missing news” do not happen at random, or are attributed to measurement failure as is, for example, the case of multimodal tasks using mechanical sensors data  Conversely, as highlighted in BIBREF7 , BIBREF8 the same price behaviour results in distinct market reactions when accompanied or not by news  In other words, specifically to financial forecasting problems the absence or existence of news are highly informative 
Some methods were proposed in the multimodal literature to effectively treat informative missing modes or “informative missingness”, which is a characteristic refereed in the literature as learning with missing modalities BIBREF22   In this work, we directly model the news missingness as a feature of our text model temporal sequence by using the method initially proposed in BIBREF45 , BIBREF46 for clinical data with missing measurements and applied in the context of financial forecasting in BIBREF47   Specifically, we implement the Zeros & Imputation (ZI) method BIBREF46 in order to jointly learn the price mode and news relationship across all days of market activity 
The ZI implementation is described as follows: Before the daily news sequence is processed by the text temporal layer (described in itm:newstclayer) we input a 0 vector for all time steps with missing news and leave the news encoding unchanged otherwise  This step is called zero imputation  In addition, we concatenate feature-wise an indicator vector with value 1 for all vectors with zero imputation and 0 for the days with news 
As described in BIBREF47 , the ZI method endow a temporal sequence model with the ability to learn different representations depending on the news history and its relative time position  Moreover, it allows our model to predict the volatility for all days of our time series and, at the same time, take into account the current and past news informative missingness  Furthermore, the learnt positional news encoding works differently than a typical “masking”, where days without news are not passed through the LSTM cell  Masking the time steps would be losing information about the presence or absence of news concomitant with prices 
Experimental results and discussions
We aim to evaluate our hierarchical neural model in the light of three main aspects  First, we asses the importance of the different sentence encoders to our end-to-end models and how it compares to transferring the sentence encoder from our two auxiliary TL tasks  Second, we ablate our proposed news relevance attention (NRA) component to evaluate its importance  Finally, we consider a model that takes into consideration only the price mode (unimodal), i e  ignoring any architecture related to the text mode 
Before we define the baselines to asses the three aspects described above, we review in the next section the scores of the trained TL tasks 
Auxiliary transfer learning tasks
This section reports the performance of the auxiliary TL tasks considered in this work  Our ultimate goal is to indicate that our scores are in line with previous works All the architectures presented in sec:transferlearning are trained for a maximum of 50 epochs using mini-batch SGD with Adam optimizer BIBREF48   Moreover, at the end of each epoch, we evaluate the validation scores, which are accuracy (Stanfor SNLI dataset) and F1 (RCV1 dataset), and save the weights with the best values  Aiming to seeped up training, we implement early stopping with patience set to 8 epochs  That is, if the validation scores do not improve for more than 10 epochs we halt the training  Finally, we use Glove pre-trained word embeddings BIBREF37 as fixed features 
tbl:tlevaluation compares our test scores with state-of-the-art (SOTA) results reported in previous works  We can see that our scores for the SNLI task are very close to state-of-the-art 
Regarding the RCV1 dataset, our results consider only the headline content for training, while the refereed works consider both the news headline and message body  The reason for training using only the headlines is that both tasks are learnt with the sole purpose of transferring the sentence encoders to our main volatility prediction task, whose textual input is restricted to headlines 
Training setup
During the training of our hierarchical neural model described in sub:HAN we took special care to guard against overfitting  To this aim, we completely separate 2016 and 2017 as the test set and report our results on this “unseen” set  The remaining data is further split into training (2007 to 2013) and validation (2014 to 2015)  The model convergence during training is monitored in the validation set  We monitor the validation score of our model at the end of each epoch and store the network weights if the validation scores improves between two consecutive epochs  Additionally, we use mini-batch SGD with Adam optimizer and early stopping with patience set to eight epochs  The hyperparameter tunning is performed using grid search 
All training is performed using the proposed global model approach described in sub:globalmodel, which learns a model that takes into account the features of all the 40 stocks of our corpus  Using this approach our training set has a total of 97,903 samples  Moreover, during the SGD mini-batch sampling the past $T$ days of price and news history tensors and each stock sample stock indicator are randomly selected from the set of all 40 stocks 
Stocks universe result
In order to evaluate the contributions of each component of our neural model described in sub:HAN and the effect of using textual data to predict the volatility, we report our results using the following baselines:
- News (unimodal price only): This baseline completely ablates (i e  removes) any architecture related to the news mode, considering only the price encoding and the stock embedding components  Using this ablation we aim to evaluate the influence of news to the volatility prediction problem 
+ News (End-to-end Sentence Encoders) - NRA: This baseline ablates our proposed new relevance attention (NRA) component, and instead, makes use of the same Daily Averaging method in BIBREF26 , BIBREF27 , where all fixed-sized headline representations on a given day are averaged without taking into account the relevance of each news  We evaluate this baseline for both BiLSTM attention (Att) and BiLSTM max-pooling (MP) sentence encoders  Here, our goal is to asses the true contribution of our NRA component in the case SOTA sentence encoders are taken into account 
+ News (End-to-End W-L Att Sentence Encoder) + NRA: The Word-Level Attention (W-L Att) sentence encoder implements an attention mechanism directly on top of word embeddings, and, as such, does not consider the order of words in a sentence  This baseline complements the previous one, i e  it evaluates the influence of the sentence encoder when our full specification is considered 
+ News (TL Sentence Encoders) + NRA: Makes use of sentence encoders of our two auxiliary TL tasks as fixed features  This baseline aims to address the following questions, namely: What dataset and models are more suitable to transfer to our specific volatility forecasting problem; How End-to-End models, which are trained on top of word embeddings, perform compared to sentence encoders transferred from other tasks 
tbl:comparativeallsectors summarizes the test scores for the ablations discussed above  Our best model is the + News (BiLSTM Att) + NRA, which is trained end-to-end and uses our full architecture  The second best model, i e  + News (BiLSTM MP) + NRA, ranks slightly lower and only differs form the best model in terms of the sentence encoder  The former sentence encoder uses an attention layer (subsec:bilstminneratt) and the the last a max-pooling layer (subsec:bilstmmaxpool), where both layers are placed on top of the LSTM hidden states of each word 
Importantly, our experiments show that using news and price (multimodal) to predict the volatility improves the scores by 11% (MSE) and 9% (MAE) when compared with the – News (price only unimodal) model that considers only price features as explanatory variables 
When comparing the performance of End-to-End models and the TL auxiliary tasks the following can be observed: The end-to-end models trained with the two SOTA sentence encoders perform better than transferring sentence encoder from both auxiliary tasks  However, our experiments show that the same does not hold for models trained end-to-end relying on the simpler WL-Att sentence encoder, which ignores the order of words in a sentence  In other words, considering the appropriate TL task, it is preferable to transfer a SOTA sentence encoder trained on a larger dataset than learning a less robust sentence encoder in an end-to-end fashion  Moreover, initially, we thought that being the RCV1 a financial domain corpus it would demonstrate a superior performance when compared to the SNLI dataset  Still, the SNLI transfers better than RCV1  We hypothesize that the text categorization task (RCV1 dataset) is not able to capture complex sentence structures at the same level required to perform natural language inference  Particularly to the volatility forecasting problem, our TL results corroborates the same findings in BIBREF17 , where it was shown that SNLI dataset attains the best sentence encoding for a broad range of pure NLP tasks, including, among other, text categorization and sentiment analysis 
Significantly, experimental results in tbl:comparativeallsectors clearly demonstrate that our proposed news relevance attention (NRA) outperforms the News Averaging method proposed in previous studies BIBREF26 , BIBREF27   Even when evaluating our NRA component in conjunction with the more elementary W-L Att sentence encoder it surpass the results of sophisticated sentence encoder using a News Averaging approach  In other words, our results strongly points to the advantage of discriminating noisy from impacting news and the effectiveness of learning to attend the most relevant news 
Having analyzed our best model, we now turn to its comparative performance with respect to the widely regarded GARCH(1,1) model described in sec:GARCH 
We asses our model performance relative to GARCH(1,1) using standard loss metrics (MSE and MAE) and the regression-based accuracy specified in eq:regressionloss and measured in terms of the coefficient of determination $R^2$   In addition, we evaluate our model across two different volatility proxies: Garman-Klass ( $\widehat{\sigma _{GK}}$ ) (eq:volgk) and Parkinson ( $\widehat{\sigma _{PK}}$ ) (eq:volpk)  We note that, as reviewed in sub:evalution, these two volatility proxies are statically efficient and proper estimators of the next day volatility 
tbl:garchallsectors reports the comparative performance among our best Price + News model (+ News BiLSTM (MP) + NRA), our Price only (unimodal) model and GARCH(1,1)  The results clearly demonstrate the superiority of our model, being more accurate than GRACH for both volatility proxies  We note that evaluating the GARCH(1,1) model relying on standard MSE and MAE error metrics should be taken with a grain of salt  BIBREF35 provides the background theory and arguments supporting $R^2$ as the metric of choice to evaluate the predictive power of a volatility model  In any case, the outperformance or our model with respect to GARCH(1,1) permeates all three metrics, name $R^2$ , $MSE$ and $MAE$  
Sector-level results
Company sectors are expected to have different risk levels, in the sense that each sector is driven by different types of news and economic cycles  Moreover, by performing a sector-level analysis we were initially interested in understanding if the outperformance of our model with respect to GARCH(1,1) was the result of a learning bias to a given sector or if, as turned out to be the case, the superior performance of our model spreads across a diversified portfolio of sectors 
In order to evaluate the performance per sector, we first separate the constituents stocks for each sector in tbl:stockuniverse  Then, we calculate the same metrics discussed in the previous section for each sector individually 
tbl:garcheachsector reports our experimental results segregated by sector  We observe that the GRACH model accuracy, measured using the $R^2$ score, has a high degree of variability among sectors  For example, the accuracy ranges from 0 15 to 0 44 for the HealthCare and Energy sector, respectively  This high degree of variability is in agreement with previous results reported in BIBREF16 , but in the context of long-term (quarterly) volatility predictions  Although the GARCH(1,1) accuracy is sector-dependent, without any exception, our model using price and news as input clearly outperforms GRACH sector-wise  This fact allow us to draw the following conclusions:
Our model outperformance is persistent across sectors, i e  the characteristics of the results reported in tbl:garchallsectors permeates all sectors, rather than being composed of a mix of outperforming and underperforming sector contributions  This fact provides a strong evidence that our model is more accurate than GARCH(1,1) 
The proposed Global model approach discussed in sub:globalmodel is able to generalize well, i e  the patterns learnt are not biased to a given sector or stock 
One of the limitations of our work is to rely on proxies for the volatility estimation  Although these proxies are handy if only open, high, low and close daily price data is available, having high frequency price data we could estimate the daily volatility using the sum of squared intraday returns to measure the true daily latent volatility  For example, in evaluating the performance for the one-day-ahead GARCH(1,1) Yen/Dollar exchange rate BIBREF35 reports $R^2$ values of 0 237 and 0 392 using hourly and five minutes sampled intraday returns, respectively  However, we believe that utilizing intraday data would further improve our model performance 
Since our experimental results demonstrate the key aspect of the news relevance attention to model architecture we observe that intraday data would arguably ameliorate the learning process  Having intraday data would allow us to pair each individual news release with the instantaneous market price reaction  Using daily data we are losing part of this information by only measuring the aggregate effect of all news to the one-day-ahead prediction 
Conclusion
We study the joint effect of stock news and prices on the daily volatility forecasting problem  To the best of our knowledge, this work is one of the first studies aiming to predict short-term (daily) rather than long-term (quarterly or yearly) volatility taking news and price as explanatory variables and using a comprehensive dataset of news headlines at the individual stock level  Our hierarchical end-to-end model benefits from state-of-the-art approaches to encode text information and to deal with two main challenges in correlating news with market reaction: news relevance and novelty  That is, to address the problem of how to attend the most important news based purely on its content (news relevance attention) and to take into account the temporal information of past news (temporal context)  Additionally, we propose a multi-stock mini-batch + stock embedding method suitable to model commonality among stocks 
The experimental results show that our multimodal approach outperforms the GARCH(1,1) volatility model, which is the most prevalent econometric model for daily volatility predictions  The outperformance being sector-wise and demonstrates the effectiveness of combining price and news for short-term volatility forecasting  The fact that we outperform GARCH(1,1) for all analyzed sectors confirms the robustness of our proposed architecture and evidences that our global model approach generalizes well 
We ablated (i e  removed) different components of our neural architecture to assess its most relevant parts  To this aim, we replaced our proposed news relevance attention layer, which aims to attend the most important news on a given day, with a simpler architecture proposed in the literature, which averages the daily news  We found that our attention layer improves the results  Additionally, we ablated all the architecture related to the news mode and found that news enhances the forecasting accuracy 
Finally, we evaluated different sentence encoders, including those transfered from other NLP tasks, and concluded that they achieve better performance as compared to a plain Word-level attention sentence encoder trained end-to-end  However, they do not beat state-of-the-art sentence encoders trained end-to-end 
In order to contribute to the literature of Universal Sentence Encoders, we evaluated the performance of transferring sentence encoders from two different tasks to the volatility prediction problem  We showed that models trained on the Natural Language Inference (NLI) task are more suitable to forecasting problems than a financial domain dataset (Reuters RCV1)  By analyzing different architectures, we showed that a BiLSTM with max-pooling for the SNLI dataset provides the best sentence encoder 
In the future, we plan to make use of intraday prices to better assess the predictive power of our proposed models  Additionally, we would further extend our analysis to other stock market sectors 

ground_truth: Energy with accuracy of 0.538

Energy