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 language does this paper focus on?
Context 1: Introduction
Text simplification aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning, which can help children, non-native speakers, and people with cognitive disabilities, to understand text better  One of the methods of automatic text simplification can be generally divided into three categories: lexical simplification (LS) BIBREF0 , BIBREF1 , rule-based BIBREF2 , and machine translation (MT) BIBREF3 , BIBREF4   LS is mainly used to simplify text by substituting infrequent and difficult words with frequent and easier words  However, there are several challenges for the LS approach: a great number of transformation rules are required for reasonable coverage and should be applied based on the specific context; third, the syntax and semantic meaning of the sentence is hard to retain  Rule-based approaches use hand-crafted rules for lexical and syntactic simplification, for example, substituting difficult words in a predefined vocabulary  However, such approaches need a lot of human-involvement to manually define these rules, and it is impossible to give all possible simplification rules  MT-based approach has attracted great attention in the last several years, which addresses text simplification as a monolingual machine translation problem translating from 'ordinary' and 'simplified' sentences 
In recent years, neural Machine Translation (NMT) is a newly-proposed deep learning approach and achieves very impressive results BIBREF5 , BIBREF6 , BIBREF7   Unlike the traditional phrased-based machine translation system which operates on small components separately, NMT system is being trained end-to-end, without the need to have external decoders, language models or phrase tables  Therefore, the existing architectures in NMT are used for text simplification BIBREF8 , BIBREF4   However, most recent work using NMT is limited to the training data that are scarce and expensive to build  Language models trained on simplified corpora have played a central role in statistical text simplification BIBREF9 , BIBREF10   One main reason is the amount of available simplified corpora typically far exceeds the amount of parallel data  The performance of models can be typically improved when trained on more data  Therefore, we expect simplified corpora to be especially helpful for NMT models 
In contrast to previous work, which uses the existing NMT models, we explore strategy to include simplified training corpora in the training process without changing the neural network architecture  We first propose to pair simplified training sentences with synthetic ordinary sentences during training, and treat this synthetic data as additional training data  We obtain synthetic ordinary sentences through back-translation, i e  an automatic translation of the simplified sentence into the ordinary sentence BIBREF11   Then, we mix the synthetic data into the original (simplified-ordinary) data to train NMT model  Experimental results on two publicly available datasets show that we can improve the text simplification quality of NMT models by mixing simplified sentences into the training set over NMT model only using the original training data 
Related Work
Automatic TS is a complicated natural language processing (NLP) task, which consists of lexical and syntactic simplification levels BIBREF12   It has attracted much attention recently as it could make texts more accessible to wider audiences, and used as a pre-processing step, improve performances of various NLP tasks and systems BIBREF13 , BIBREF14 , BIBREF15   Usually, hand-crafted, supervised, and unsupervised methods based on resources like English Wikipedia and Simple English Wikipedia (EW-SEW) BIBREF10 are utilized for extracting simplification rules  It is very easy to mix up the automatic TS task and the automatic summarization task BIBREF3 , BIBREF16 , BIBREF6   TS is different from text summarization as the focus of text summarization is to reduce the length and redundant content 
At the lexical level, lexical simplification systems often substitute difficult words using more common words, which only require a large corpus of regular text to obtain word embeddings to get words similar to the complex word BIBREF1 , BIBREF9   Biran et al  BIBREF0 adopted an unsupervised method for learning pairs of complex and simpler synonyms from a corpus consisting of Wikipedia and Simple Wikipedia  At the sentence level, a sentence simplification model was proposed by tree transformation based on statistical machine translation (SMT) BIBREF3   Woodsend and Lapata BIBREF17 presented a data-driven model based on a quasi-synchronous grammar, a formalism that can naturally capture structural mismatches and complex rewrite operations  Wubben et al  BIBREF18 proposed a phrase-based machine translation (PBMT) model that is trained on ordinary-simplified sentence pairs  Xu et al  BIBREF19 proposed a syntax-based machine translation model using simplification-specific objective functions and features to encourage simpler output 
Compared with SMT, neural machine translation (NMT) has shown to produce state-of-the-art results BIBREF5 , BIBREF7   The central approach of NMT is an encoder-decoder architecture implemented by recurrent neural networks, which can represent the input sequence as a vector, and then decode that vector into an output sequence  Therefore, NMT models were used for text simplification task, and achieved good results BIBREF8 , BIBREF4 , BIBREF20   The main limitation of the aforementioned NMT models for text simplification depended on the parallel ordinary-simplified sentence pairs  Because ordinary-simplified sentence pairs are expensive and time-consuming to build, the available largest data is EW-SEW that only have 296,402 sentence pairs  The dataset is insufficiency for NMT model if we want to NMT model can obtain the best parameters  Considering simplified data plays an important role in boosting fluency for phrase-based text simplification, and we investigate the use of simplified data for text simplification  We are the first to show that we can effectively adapt neural translation models for text simplifiation with simplified corpora 
Simplified Corpora
We collected a simplified dataset from Simple English Wikipedia that are freely available, which has been previously used for many text simplification methods BIBREF0 , BIBREF10 , BIBREF3   The simple English Wikipedia is pretty easy to understand than normal English Wikipedia  We downloaded all articles from Simple English Wikipedia  For these articles, we removed stubs, navigation pages and any article that consisted of a single sentence  We then split them into sentences with the Stanford CorNLP BIBREF21 , and deleted these sentences whose number of words are smaller than 10 or large than 40  After removing repeated sentences, we chose 600K sentences as the simplified data with 11 6M words, and the size of vocabulary is 82K 
Text Simplification using Neural Machine Translation
Our work is built on attention-based NMT BIBREF5 as an encoder-decoder network with recurrent neural networks (RNN), which simultaneously conducts dynamic alignment and generation of the target simplified sentence 
The encoder uses a bidirectional RNN that consists of forward and backward RNN  Given a source sentence INLINEFORM0 , the forward RNN and backward RNN calculate forward hidden states INLINEFORM1 and backward hidden states INLINEFORM2 , respectively  The annotation vector INLINEFORM3 is obtained by concatenating INLINEFORM4 and INLINEFORM5  
The decoder is a RNN that predicts a target simplificated sentence with Gated Recurrent Unit (GRU) BIBREF22   Given the previously generated target (simplified) sentence INLINEFORM0 , the probability of next target word INLINEFORM1 is DISPLAYFORM0
where INLINEFORM0 is a non-linear function, INLINEFORM1 is the embedding of INLINEFORM2 , and INLINEFORM3 is a decoding state for time step INLINEFORM4  
State INLINEFORM0 is calculated by DISPLAYFORM0
where INLINEFORM0 is the activation function GRU 
The INLINEFORM0 is the context vector computed as a weighted annotation INLINEFORM1 , computed by DISPLAYFORM0
where the weight INLINEFORM0 is computed by DISPLAYFORM0 DISPLAYFORM1
where INLINEFORM0 , INLINEFORM1 and INLINEFORM2 are weight matrices  The training objective is to maximize the likelihood of the training data  Beam search is employed for decoding 
Synthetic Simplified Sentences
We train an auxiliary system using NMT model from the simplified sentence to the ordinary sentence, which is first trained on the available parallel data  For leveraging simplified sentences to improve the quality of NMT model for text simplification, we propose to adapt the back-translation approach proposed by Sennrich et al  BIBREF11 to our scenario  More concretely, Given one sentence in simplified sentences, we use the simplified-ordinary system in translate mode with greedy decoding to translate it to the ordinary sentences, which is denoted as back-translation  This way, we obtain a synthetic parallel simplified-ordinary sentences  Both the synthetic sentences and the available parallel data are used as training data for the original NMT system 
Evaluation
We evaluate the performance of text simplification using neural machine translation on available parallel sentences and additional simplified sentences 
Dataset  We use two simplification datasets (WikiSmall and WikiLarge)  WikiSmall consists of ordinary and simplified sentences from the ordinary and simple English Wikipedias, which has been used as benchmark for evaluating text simplification BIBREF17 , BIBREF18 , BIBREF8   The training set has 89,042 sentence pairs, and the test set has 100 pairs  WikiLarge is also from Wikipedia corpus whose training set contains 296,402 sentence pairs BIBREF19 , BIBREF20   WikiLarge includes 8 (reference) simplifications for 2,359 sentences split into 2,000 for development and 359 for testing 
Metrics  Three metrics in text simplification are chosen in this paper  BLEU BIBREF5 is one traditional machine translation metric to assess the degree to which translated simplifications differed from reference simplifications  FKGL measures the readability of the output BIBREF23   A small FKGL represents simpler output  SARI is a recent text-simplification metric by comparing the output against the source and reference simplifications BIBREF20  
We evaluate the output of all systems using human evaluation  The metric is denoted as Simplicity BIBREF8   The three non-native fluent English speakers are shown reference sentences and output sentences  They are asked whether the output sentence is much simpler (+2), somewhat simpler (+1), equally (0), somewhat more difficult (-1), and much more difficult (-2) than the reference sentence 
Methods  We use OpenNMT BIBREF24 as the implementation of the NMT system for all experiments BIBREF5   We generally follow the default settings and training procedure described by Klein et al (2017)  We replace out-of-vocabulary words with a special UNK symbol  At prediction time, we replace UNK words with the highest probability score from the attention layer  OpenNMT system used on parallel data is the baseline system  To obtain a synthetic parallel training set, we back-translate a random sample of 100K sentences from the collected simplified corpora  OpenNMT used on parallel data and synthetic data is our model  The benchmarks are run on a Intel(R) Core(TM) i7-5930K CPU@3 50GHz, 32GB Mem, trained on 1 GPU GeForce GTX 1080 (Pascal) with CUDA v  8 0 
We choose three statistical text simplification systems  PBMT-R is a phrase-based method with a reranking post-processing step BIBREF18   Hybrid performs sentence splitting and deletion operations based on discourse representation structures, and then simplifies sentences with PBMT-R BIBREF25   SBMT-SARI BIBREF19 is syntax-based translation model using PPDB paraphrase database BIBREF26 and modifies tuning function (using SARI)  We choose two neural text simplification systems  NMT is a basic attention-based encoder-decoder model which uses OpenNMT framework to train with two LSTM layers, hidden states of size 500 and 500 hidden units, SGD optimizer, and a dropout rate of 0 3 BIBREF8   Dress is an encoder-decoder model coupled with a deep reinforcement learning framework, and the parameters are chosen according to the original paper BIBREF20   For the experiments with synthetic parallel data, we back-translate a random sample of 60 000 sentences from the collected simplified sentences into ordinary sentences  Our model is trained on synthetic data and the available parallel data, denoted as NMT+synthetic 
Results  Table 1 shows the results of all models on WikiLarge dataset  We can see that our method (NMT+synthetic) can obtain higher BLEU, lower FKGL and high SARI compared with other models, except Dress on FKGL and SBMT-SARI on SARI  It verified that including synthetic data during training is very effective, and yields an improvement over our baseline NMF by 2 11 BLEU, 1 7 FKGL and 1 07 SARI  We also substantially outperform Dress, who previously reported SOTA result  The results of our human evaluation using Simplicity are also presented in Table 1  NMT on synthetic data is significantly better than PBMT-R, Dress, and SBMT-SARI on Simplicity  It indicates that our method with simplified data is effective at creating simpler output 
Results on WikiSmall dataset are shown in Table 2  We see substantial improvements (6 37 BLEU) than NMT from adding simplified training data with synthetic ordinary sentences  Compared with statistical machine translation models (PBMT-R, Hybrid, SBMT-SARI), our method (NMT+synthetic) still have better results, but slightly worse FKGL and SARI  Similar to the results in WikiLarge, the results of our human evaluation using Simplicity outperforms the other models  In conclusion, Our method produces better results comparing with the baselines, which demonstrates the effectiveness of adding simplified training data 
Conclusion
In this paper, we propose one simple method to use simplified corpora during training of NMT systems, with no changes to the network architecture  In the experiments on two datasets, we achieve substantial gains in all tasks, and new SOTA results, via back-translation of simplified sentences into the ordinary sentences, and treating this synthetic data as additional training data  Because we do not change the neural network architecture to integrate simplified corpora, our method can be easily applied to other Neural Text Simplification (NTS) systems  We expect that the effectiveness of our method not only varies with the quality of the NTS system used for back-translation, but also depends on the amount of available parallel and simplified corpora  In the paper, we have only utilized data from Wikipedia for simplified sentences  In the future, many other text sources are available and the impact of not only size, but also of domain should be investigated 
Context 2: 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  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 
Context 3: Introduction
Privacy policies are the documents which disclose the ways in which a company gathers, uses, shares and manages a user's data  As legal documents, they function using the principle of notice and choice BIBREF0, where companies post their policies, and theoretically, users read the policies and decide to use a company's products or services only if they find the conditions outlined in its privacy policy acceptable  Many legal jurisdictions around the world accept this framework, including the United States and the European Union BIBREF1, BIBREF2  However, the legitimacy of this framework depends upon users actually reading and understanding privacy policies to determine whether company practices are acceptable to them BIBREF3  In practice this is seldom the case BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10  This is further complicated by the highly individual and nuanced compromises that users are willing to make with their data BIBREF11, discouraging a `one-size-fits-all' approach to notice of data practices in privacy documents 
With devices constantly monitoring our environment, including our personal space and our bodies, lack of awareness of how our data is being used easily leads to problematic situations where users are outraged by information misuse, but companies insist that users have consented  The discovery of increasingly egregious uses of data by companies, such as the scandals involving Facebook and Cambridge Analytica BIBREF12, have further brought public attention to the privacy concerns of the internet and ubiquitous computing  This makes privacy a well-motivated application domain for NLP researchers, where advances in enabling users to quickly identify the privacy issues most salient to them can potentially have large real-world impact 
[1]https://play google com/store/apps/details?id=com gotokeep keep intl [2]https://play google com/store/apps/details?id=com viber voip [3]A question might not have any supporting evidence for an answer within the privacy policy 
Motivated by this need, we contribute PrivacyQA, a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations  The goal of this effort is to kickstart the development of question-answering methods for this domain, to address the (unrealistic) expectation that a large population should be reading many policies per day  In doing so, we identify several understudied challenges to our ability to answer these questions, with broad implications for systems seeking to serve users' information-seeking intent  By releasing this resource, we hope to provide an impetus to develop systems capable of language understanding in this increasingly important domain 
Related Work
Prior work has aimed to make privacy policies easier to understand  Prescriptive approaches towards communicating privacy information BIBREF21, BIBREF22, BIBREF23 have not been widely adopted by industry  Recently, there have been significant research effort devoted to understanding privacy policies by leveraging NLP techniques BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28, especially by identifying specific data practices within a privacy policy  We adopt a personalized approach to understanding privacy policies, that allows users to query a document and selectively explore content salient to them  Most similar is the PolisisQA corpus BIBREF29, which examines questions users ask corporations on Twitter  Our approach differs in several ways: 1) The PrivacyQA dataset is larger, containing 10x as many questions and answers  2) Answers are formulated by domain experts with legal training  3) PrivacyQA includes diverse question types, including unanswerable and subjective questions 
Our work is also related to reading comprehension in the open domain, which is frequently based upon Wikipedia passages BIBREF16, BIBREF17, BIBREF15, BIBREF30 and news articles BIBREF20, BIBREF31, BIBREF32  Table TABREF4 presents the desirable attributes our dataset shares with past approaches  This work is also tied into research in applying NLP approaches to legal documents BIBREF33, BIBREF34, BIBREF35, BIBREF36, BIBREF37, BIBREF38, BIBREF39  While privacy policies have legal implications, their intended audience consists of the general public rather than individuals with legal expertise  This arrangement is problematic because the entities that write privacy policies often have different goals than the audience  feng2015applying, tan-EtAl:2016:P16-1 examine question answering in the insurance domain, another specialized domain similar to privacy, where the intended audience is the general public 
Data Collection
We describe the data collection methodology used to construct PrivacyQA  With the goal of achieving broad coverage across application types, we collect privacy policies from 35 mobile applications representing a number of different categories in the Google Play Store  One of our goals is to include both policies from well-known applications, which are likely to have carefully-constructed privacy policies, and lesser-known applications with smaller install bases, whose policies might be considerably less sophisticated  Thus, setting 5 million installs as a threshold, we ensure each category includes applications with installs on both sides of this threshold  All policies included in the corpus are in English, and were collected before April 1, 2018, predating many companies' GDPR-focused BIBREF41 updates  We leave it to future studies BIBREF42 to look at the impact of the GDPR (e g , to what extent GDPR requirements contribute to making it possible to provide users with more informative answers, and to what extent their disclosures continue to omit issues that matter to users) 
Data Collection ::: Crowdsourced Question Elicitation
The intended audience for privacy policies consists of the general public  This informs the decision to elicit questions from crowdworkers on the contents of privacy policies  We choose not to show the contents of privacy policies to crowdworkers, a procedure motivated by a desire to avoid inadvertent biases BIBREF43, BIBREF44, BIBREF45, BIBREF46, BIBREF47, and encourage crowdworkers to ask a variety of questions beyond only asking questions based on practices described in the document 
Instead, crowdworkers are presented with public information about a mobile application available on the Google Play Store including its name, description and navigable screenshots  Figure FIGREF9 shows an example of our user interface  Crowdworkers are asked to imagine they have access to a trusted third-party privacy assistant, to whom they can ask any privacy question about a given mobile application  We use the Amazon Mechanical Turk platform and recruit crowdworkers who have been conferred “master” status and are located within the United States of America  Turkers are asked to provide five questions per mobile application, and are paid $2 per assignment, taking ~eight minutes to complete the task 
Data Collection ::: Answer Selection
To identify legally sound answers, we recruit seven experts with legal training to construct answers to Turker questions  Experts identify relevant evidence within the privacy policy, as well as provide meta-annotation on the question's relevance, subjectivity, OPP-115 category BIBREF49, and how likely any privacy policy is to contain the answer to the question asked 
Data Collection ::: Analysis
Table TABREF17 presents aggregate statistics of the PrivacyQA dataset  1750 questions are posed to our imaginary privacy assistant over 35 mobile applications and their associated privacy documents  As an initial step, we formulate the problem of answering user questions as an extractive sentence selection task, ignoring for now background knowledge, statistical data and legal expertise that could otherwise be brought to bear  The dataset is partitioned into a training set featuring 27 mobile applications and 1350 questions, and a test set consisting of 400 questions over 8 policy documents  This ensures that documents in training and test splits are mutually exclusive  Every question is answered by at least one expert  In addition, in order to estimate annotation reliability and provide for better evaluation, every question in the test set is answered by at least two additional experts 
Table TABREF14 describes the distribution over first words of questions posed by crowdworkers  We also observe low redundancy in the questions posed by crowdworkers over each policy, with each policy receiving ~49 94 unique questions despite crowdworkers independently posing questions  Questions are on average 8 4 words long

ground_truth: English

Simple 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. Which stock market sector achieved the best performance?
Context 3:  As declining to answer a question can be a legally sound response but is seldom practically useful, answers to questions where a minority of experts abstain to answer are filtered from the dataset  Privacy policies are ~3000 words long on average  The answers to the question asked by the users typically have ~100 words of evidence in the privacy policy document 
Data Collection ::: Analysis ::: Categories of Questions
Questions are organized under nine categories from the OPP-115 Corpus annotation scheme BIBREF49:
First Party Collection/Use: What, why and how information is collected by the service provider
Third Party Sharing/Collection: What, why and how information shared with or collected by third parties
Data Security: Protection measures for user information
Data Retention: How long user information will be stored
User Choice/Control: Control options available to users
User Access, Edit and Deletion: If/how users can access, edit or delete information
Policy Change: Informing users if policy information has been changed
International and Specific Audiences: Practices pertaining to a specific group of users
Other: General text, contact information or practices not covered by other categories 
For each question, domain experts indicate one or more relevant OPP-115 categories  We mark a category as relevant to a question if it is identified as such by at least two annotators  If no such category exists, the category is marked as `Other' if atleast one annotator has identified the `Other' category to be relevant  If neither of these conditions is satisfied, we label the question as having no agreement  The distribution of questions in the corpus across OPP-115 categories is as shown in Table TABREF16  First party and third party related questions are the largest categories, forming nearly 66 4% of all questions asked to the privacy assistant 
Data Collection ::: Analysis ::: Answer Validation
When do experts disagree? We would like to analyze the reasons for potential disagreement on the annotation task, to ensure disagreements arise due to valid differences in opinion rather than lack of adequate specification in annotation guidelines  It is important to note that the annotators are experts rather than crowdworkers  Accordingly, their judgements can be considered valid, legally-informed opinions even when their perspectives differ  For the sake of this question we randomly sample 100 instances in the test data and analyze them for likely reasons for disagreements  We consider a disagreement to have occurred when more than one expert does not agree with the majority consensus  By disagreement we mean there is no overlap between the text identified as relevant by one expert and another 
We find that the annotators agree on the answer for 74% of the questions, even if the supporting evidence they identify is not identical i e full overlap  They disagree on the remaining 26%  Sources of apparent disagreement correspond to situations when different experts: have differing interpretations of question intent (11%) (for example, when a user asks 'who can contact me through the app', the questions admits multiple interpretations, including seeking information about the features of the app, asking about first party collection/use of data or asking about third party collection/use of data), identify different sources of evidence for questions that ask if a practice is performed or not (4%), have differing interpretations of policy content (3%), identify a partial answer to a question in the privacy policy (2%) (for example, when the user asks `who is allowed to use the app' a majority of our annotators decline to answer, but the remaining annotators highlight partial evidence in the privacy policy which states that children under the age of 13 are not allowed to use the app), and other legitimate sources of disagreement (6%) which include personal subjective views of the annotators (for example, when the user asks `is my DNA information used in any way other than what is specified', some experts consider the boilerplate text of the privacy policy which states that it abides to practices described in the policy document as sufficient evidence to answer this question, whereas others do not) 
Experimental Setup
We evaluate the ability of machine learning methods to identify relevant evidence for questions in the privacy domain  We establish baselines for the subtask of deciding on the answerability (§SECREF33) of a question, as well as the overall task of identifying evidence for questions from policies (§SECREF37)  We describe aspects of the question that can render it unanswerable within the privacy domain (§SECREF41) 
Experimental Setup ::: Answerability Identification Baselines
We define answerability identification as a binary classification task, evaluating model ability to predict if a question can be answered, given a question in isolation  This can serve as a prior for downstream question-answering  We describe three baselines on the answerability task, and find they considerably improve performance over a majority-class baseline 
SVM: We define 3 sets of features to characterize each question  The first is a simple bag-of-words set of features over the question (SVM-BOW), the second is bag-of-words features of the question as well as length of the question in words (SVM-BOW + LEN), and lastly we extract bag-of-words features, length of the question in words as well as part-of-speech tags for the question (SVM-BOW + LEN + POS)  This results in vectors of 200, 201 and 228 dimensions respectively, which are provided to an SVM with a linear kernel 
CNN: We utilize a CNN neural encoder for answerability prediction  We use GloVe word embeddings BIBREF50, and a filter size of 5 with 64 filters to encode questions 
BERT: BERT BIBREF51 is a bidirectional transformer-based language-model BIBREF52  We fine-tune BERT-base on our binary answerability identification task with a learning rate of 2e-5 for 3 epochs, with a maximum sequence length of 128 
Experimental Setup ::: Privacy Question Answering
Our goal is to identify evidence within a privacy policy for questions asked by a user  This is framed as an answer sentence selection task, where models identify a set of evidence sentences from all candidate sentences in each policy 
Experimental Setup ::: Privacy Question Answering ::: Evaluation Metric
Our evaluation metric for answer-sentence selection is sentence-level F1, implemented similar to BIBREF30, BIBREF16  Precision and recall are implemented by measuring the overlap between predicted sentences and sets of gold-reference sentences  We report the average of the maximum F1 from each n$-$1 subset, in relation to the heldout reference 
Experimental Setup ::: Privacy Question Answering ::: Baselines
We describe baselines on this task, including a human performance baseline 
No-Answer Baseline (NA) : Most of the questions we receive are difficult to answer in a legally-sound way on the basis of information present in the privacy policy  We establish a simple baseline to quantify the effect of identifying every question as unanswerable 
Word Count Baseline : To quantify the effect of using simple lexical matching to answer the questions, we retrieve the top candidate policy sentences for each question using a word count baseline BIBREF53, which counts the number of question words that also appear in a sentence  We include the top 2, 3 and 5 candidates as baselines 
BERT: We implement two BERT-based baselines BIBREF51 for evidence identification  First, we train BERT on each query-policy sentence pair as a binary classification task to identify if the sentence is evidence for the question or not (Bert)  We also experiment with a two-stage classifier, where we separately train the model on questions only to predict answerability  At inference time, if the answerable classifier predicts the question is answerable, the evidence identification classifier produces a set of candidate sentences (Bert + Unanswerable) 
Human Performance: We pick each reference answer provided by an annotator, and compute the F1 with respect to the remaining references, as described in section 4 2 1  Each reference answer is treated as the prediction, and the remaining n-1 answers are treated as the gold reference  The average of the maximum F1 across all reference answers is computed as the human baseline 
Results and Discussion
The results of the answerability baselines are presented in Table TABREF31, and on answer sentence selection in Table TABREF32  We observe that bert exhibits the best performance on a binary answerability identification task  However, most baselines considerably exceed the performance of a majority-class baseline  This suggests considerable information in the question, indicating it's possible answerability within this domain 
Table TABREF32 describes the performance of our baselines on the answer sentence selection task  The No-answer (NA) baseline performs at 28 F1, providing a lower bound on performance at this task  We observe that our best-performing baseline, Bert + Unanswerable achieves an F1 of 39 8  This suggest that bert is capable of making some progress towards answering questions in this difficult domain, while still leaving considerable headroom for improvement to reach human performance  Bert + Unanswerable performance suggests that incorporating information about answerability can help in this difficult domain  We examine this challenging phenomena of unanswerability further in Section  
Results and Discussion ::: Error Analysis
Disagreements are analyzed based on the OPP-115 categories of each question (Table TABREF34)  We compare our best performing BERT variant against the NA model and human performance

ground_truth: Energy with accuracy of 0.538

Energy

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. Who were the experts used for annotation?
Context 3:  We observe significant room for improvement across all categories of questions but especially for first party, third party and data retention categories 
We analyze the performance of our strongest BERT variant, to identify classes of errors and directions for future improvement (Table 8)  We observe that a majority of answerability mistakes made by the BERT model are questions which are in fact answerable, but are identified as unanswerable by BERT  We observe that BERT makes 124 such mistakes on the test set  We collect expert judgments on relevance, subjectivity , silence and information about how likely the question is to be answered from the privacy policy from our experts  We find that most of these mistakes are relevant questions  However many of them were identified as subjective by the annotators, and at least one annotator marked 19 of these questions as having no answer within the privacy policy  However, only 6 of these questions were unexpected or do not usually have an answer in privacy policies  These findings suggest that a more nuanced understanding of answerability might help improve model performance in his challenging domain 
Results and Discussion ::: What makes Questions Unanswerable?
We further ask legal experts to identify potential causes of unanswerability of questions  This analysis has considerable implications  While past work BIBREF17 has treated unanswerable questions as homogeneous, a question answering system might wish to have different treatments for different categories of `unanswerable' questions  The following factors were identified to play a role in unanswerability:
Incomprehensibility: If a question is incomprehensible to the extent that its meaning is not intelligible 
Relevance: Is this question in the scope of what could be answered by reading the privacy policy 
Ill-formedness: Is this question ambiguous or vague  An ambiguous statement will typically contain expressions that can refer to multiple potential explanations, whereas a vague statement carries a concept with an unclear or soft definition 
Silence: Other policies answer this type of question but this one does not 
Atypicality: The question is of a nature such that it is unlikely for any policy policy to have an answer to the question 
Our experts attempt to identify the different `unanswerable' factors for all 573 such questions in the corpus  4 18% of the questions were identified as being incomprehensible (for example, `any difficulties to occupy the privacy assistant')  Amongst the comprehendable questions, 50% were identified as likely to have an answer within the privacy policy, 33 1% were identified as being privacy-related questions but not within the scope of a privacy policy (e g , 'has Viber had any privacy breaches in the past?') and 16 9% of questions were identified as completely out-of-scope (e g , `'will the app consume much space?')  In the questions identified as relevant, 32% were ill-formed questions that were phrased by the user in a manner considered vague or ambiguous  Of the questions that were both relevant as well as `well-formed', 95 7% of the questions were not answered by the policy in question but it was reasonable to expect that a privacy policy would contain an answer  The remaining 4 3% were described as reasonable questions, but of a nature generally not discussed in privacy policies  This suggests that the answerability of questions over privacy policies is a complex issue, and future systems should consider each of these factors when serving user's information seeking intent 
We examine a large-scale dataset of “natural” unanswerable questions BIBREF54 based on real user search engine queries to identify if similar unanswerability factors exist  It is important to note that these questions have previously been filtered, according to a criteria for bad questions defined as “(questions that are) ambiguous, incomprehensible, dependent on clear false presuppositions, opinion-seeking, or not clearly a request for factual information ” Annotators made the decision based on the content of the question without viewing the equivalent Wikipedia page  We randomly sample 100 questions from the development set which were identified as unanswerable, and find that 20% of the questions are not questions (e g , “all I want for christmas is you mariah carey tour”)  12% of questions are unlikely to ever contain an answer on Wikipedia, corresponding closely to our atypicality category  3% of questions are unlikely to have an answer anywhere (e g , `what guides Santa home after he has delivered presents?')  7% of questions are incomplete or open-ended (e g , `the south west wind blows across nigeria between')  3% of questions have an unresolvable coreference (e g , `how do i get to Warsaw Missouri from here')  4% of questions are vague, and a further 7% have unknown sources of error  2% still contain false presuppositions (e g , `what is the only fruit that does not have seeds?') and the remaining 42% do not have an answer within the document  This reinforces our belief that though they have been understudied in past work, any question answering system interacting with real users should expect to receive such unanticipated and unanswerable questions 
Conclusion
We present PrivacyQA, the first significant corpus of privacy policy questions and more than 3500 expert annotations of relevant answers  The goal of this work is to promote question-answering research in the specialized privacy domain, where it can have large real-world impact  Strong neural baselines on PrivacyQA achieve a performance of only 39 8 F1 on this corpus, indicating considerable room for future research  Further, we shed light on several important considerations that affect the answerability of questions  We hope this contribution leads to multidisciplinary efforts to precisely understand user intent and reconcile it with information in policy documents, from both the privacy and NLP communities 
Acknowledgements
This research was supported in part by grants from the National Science Foundation Secure and Trustworthy Computing program (CNS-1330596, CNS-1330214, CNS-15-13957, CNS-1801316, CNS-1914486, CNS-1914444) and a DARPA Brandeis grant on Personalized Privacy Assistants (FA8750-15-2-0277)  The US Government is authorized to reproduce and distribute reprints for Governmental purposes not withstanding any copyright notation  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the NSF, DARPA, or the US Government  The authors would like to extend their gratitude to Elias Wright, Gian Mascioli, Kiara Pillay, Harrison Kay, Eliel Talo, Alexander Fagella and N  Cameron Russell for providing their valuable expertise and insight to this effort  The authors are also grateful to Eduard Hovy, Lorrie Cranor, Florian Schaub, Joel Reidenberg, Aditya Potukuchi and Igor Shalyminov for helpful discussions related to this work, and to the three anonymous reviewers of this draft for their constructive feedback  Finally, the authors would like to thank all crowdworkers who consented to participate in this study 

ground_truth: Individuals with legal training

Yes