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

Please answer the questions based on the text provided below. How does proposed qualitative annotation schema looks like?
Context 1: Introduction
There is a recent spark of interest in the task of Question Answering (QA) over unstructured textual data, also referred to as Machine Reading Comprehension (MRC)  This is mostly due to wide-spread success of advances in various facets of deep learning related research, such as novel architectures BIBREF0, BIBREF1 that allow for efficient optimisation of neural networks consisting of multiple layers, hardware designed for deep learning purposes and software frameworks BIBREF2, BIBREF3 that allow efficient development and testing of novel approaches  These factors enable researchers to produce models that are pre-trained on large scale corpora and provide contextualised word representations BIBREF4 that are shown to be a vital component towards solutions for a variety of natural language understanding tasks, including MRC BIBREF5  Another important factor that led to the recent success in MRC-related tasks is the widespread availability of various large datasets, e g , SQuAD BIBREF6, that provide sufficient examples for optimising statistical models  The combination of these factors yields notable results, even surpassing human performance BIBREF7 
MRC is a generic task format that can be used to probe for various natural language understanding capabilities BIBREF8  Therefore it is crucially important to establish a rigorous evaluation methodology in order to be able to draw reliable conclusions from conducted experiments  While increasing effort is put into the evaluation of novel architectures, such as keeping the evaluation data from public access to prevent unintentional overfitting to test data, performing ablation and error studies and introducing novel metrics BIBREF9, surprisingly little is done to establish the quality of the data itself  Additionally, recent research arrived at worrisome findings: the data of those gold standards, which is usually gathered involving a crowd-sourcing step, suffers from flaws in design BIBREF10 or contains overly specific keywords BIBREF11  Furthermore, these gold standards contain “annotation artefacts”, cues that lead models into focusing on superficial aspects of text, such as lexical overlap and word order, instead of actual language understanding BIBREF12, BIBREF13  These weaknesses cast some doubt on whether the data can reliably evaluate the reading comprehension performance of the models they evaluate, i e  if the models are indeed being assessed for their capability to read 
Figure FIGREF3 shows an example from HotpotQA BIBREF14, a dataset that exhibits the last kind of weakness mentioned above, i e , the presence of unique keywords in both the question and the passage (in close proximity to the expected answer) 
An evaluation methodology is vital to the fine-grained understanding of challenges associated with a single gold standard, in order to understand in greater detail which capabilities of MRC models it evaluates  More importantly, it allows to draw comparisons between multiple gold standards and between the results of respective state-of-the-art models that are evaluated on them 
In this work, we take a step back and propose a framework to systematically analyse MRC evaluation data, typically a set of questions and expected answers to be derived from accompanying passages  Concretely, we introduce a methodology to categorise the linguistic complexity of the textual data and the reasoning and potential external knowledge required to obtain the expected answer  Additionally we propose to take a closer look at the factual correctness of the expected answers, a quality dimension that appears under-explored in literature 
We demonstrate the usefulness of the proposed framework by applying it to precisely describe and compare six contemporary MRC datasets  Our findings reveal concerns about their factual correctness, the presence of lexical cues that simplify the task of reading comprehension and the lack of semantic altering grammatical modifiers  We release the raw data comprised of 300 paragraphs, questions and answers richly annotated under the proposed framework as a resource for researchers developing natural language understanding models and datasets to utilise further 
To the best of our knowledge this is the first attempt to introduce a common evaluation methodology for MRC gold standards and the first across-the-board qualitative evaluation of MRC datasets with respect to the proposed categories 
Framework for MRC Gold Standard Analysis ::: Problem definition
We define the task of machine reading comprehension, the target application of the proposed methodology as follows: Given a paragraph $P$ that consists of tokens (words) $p_1, \ldots , p_{n_P}$ and a question $Q$ that consists of tokens $q_1 \ldots q_{n_Q}$, the goal is to retrieve an answer $A$ with tokens $a_1 \ldots a_{n_A}$  $A$ is commonly constrained to be one of the following cases BIBREF15, illustrated in Figure FIGREF9:
Multiple choice, where the goal is to predict $A$ from a given set of choices $\mathcal {A}$ 
Cloze-style, where $S$ is a sentence, and $A$ and $Q$ are obtained by removing a sequence of words such that $Q = S - A$  The task is to fill in the resulting gap in $Q$ with the expected answer $A$ to form $S$ 
Span, where is a continuous subsequence of tokens from the paragraph ($A \subseteq P$)  Flavours include multiple spans as the correct answer or $A \subseteq Q$ 
Free form, where $A$ is an unconstrained natural language string 
A gold standard $G$ is composed of $m$ entries $(Q_i, A_i, P_i)_{i\in \lbrace 1,\ldots , m\rbrace }$ 
The performance of an approach is established by comparing its answer predictions $A^*_{i}$ on the given input $(Q_i, T_i)$ (and $\mathcal {A}_i$ for the multiple choice setting) against the expected answer $A_i$ for all $i\in \lbrace 1,\ldots , m\rbrace $ under a performance metric  Typical performance metrics are exact match (EM) or accuracy, i e  the percentage of exactly predicted answers, and the F1 score – the harmonic mean between the precision and the recall of the predicted tokens compared to expected answer tokens  The overall F1 score can either be computed by averaging the F1 scores for every instance or by first averaging the precision and recall and then computing the F1 score from those averages (macro F1)  Free-text answers, meanwhile, are evaluated by means of text generation and summarisation metrics such as BLEU BIBREF16 or ROUGE-L BIBREF17 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest
In this section we describe a methodology to categorise gold standards according to linguistic complexity, required reasoning and background knowledge, and their factual correctness  Specifically, we use those dimensions as high-level categories of a qualitative annotation schema for annotating question, expected answer and the corresponding context  We further enrich the qualitative annotations by a metric based on lexical cues in order to approximate a lower bound for the complexity of the reading comprehension task  By sampling entries from each gold standard and annotating them, we obtain measurable results and thus are able to make observations about the challenges present in that gold standard data 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Problem setting
We are interested in different types of the expected answer  We differentiate between Span, where an answer is a continuous span taken from the passage, Paraphrasing, where the answer is a paraphrase of a text span, Unanswerable, where there is no answer present in the context, and Generated, if it does not fall into any of the other categories  It is not sufficient for an answer to restate the question or combine multiple Span or Paraphrasing answers to be annotated as Generated  It is worth mentioning that we focus our investigations on answerable questions  For a complementary qualitative analysis that categorises unanswerable questions, the reader is referred to Yatskar2019 
Furthermore, we mark a sentence as Supporting Fact if it contains evidence required to produce the expected answer, as they are used further in the complexity analysis 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Factual Correctness
An important factor for the quality of a benchmark is its factual correctness, because on the one hand, the presence of factually wrong or debatable examples introduces an upper bound for the achievable performance of models on those gold standards  On the other hand, it is hard to draw conclusions about the correctness of answers produced by a model that is evaluated on partially incorrect data 
One way by which developers of modern crowd-sourced gold standards ensure quality is by having the same entry annotated by multiple workers BIBREF18 and keeping only those with high agreement  We investigate whether this method is enough to establish a sound ground truth answer that is unambiguously correct  Concretely we annotate an answer as Debatable when the passage features multiple plausible answers, when multiple expected answers contradict each other, or an answer is not specific enough with respect to the question and a more specific answer is present  We annotate an answer as Wrong when it is factually wrong and a correct answer is present in the context 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Required Reasoning
It is important to understand what types of reasoning the benchmark evaluates, in order to be able to accredit various reasoning capabilities to the models it evaluates  Our proposed reasoning categories are inspired by those found in scientific question answering literature BIBREF19, BIBREF20, as research in this area focuses on understanding the required reasoning capabilities  We include reasoning about the Temporal succession of events, Spatial reasoning about directions and environment, and Causal reasoning about the cause-effect relationship between events  We further annotate (multiple-choice) answers that can only be answered By Exclusion of every other alternative 
We further extend the reasoning categories by operational logic, similar to those required in semantic parsing tasks BIBREF21, as solving those tasks typically requires “multi-hop” reasoning BIBREF14, BIBREF22  When an answer can only be obtained by combining information from different sentences joined by mentioning a common entity, concept, date, fact or event (from here on called entity), we annotate it as Bridge  We further annotate the cases, when the answer is a concrete entity that satisfies a Constraint specified in the question, when it is required to draw a Comparison of multiple entities' properties or when the expected answer is an Intersection of their properties (e g  “What do Person A and Person B have in common?”)
We are interested in the linguistic reasoning capabilities probed by a gold standard, therefore we include the appropriate category used by Wang2019  Specifically, we annotate occurrences that require understanding of Negation, Quantifiers (such as “every”, “some”, or “all”), Conditional (“if    then”) statements and the logical implications of Con-/Disjunction (i e  “and” and “or”) in order to derive the expected answer 
Finally, we investigate whether arithmetic reasoning requirements emerge in MRC gold standards as this can probe for reasoning that is not evaluated by simple answer retrieval BIBREF23  To this end, we annotate the presence of of Addition and Subtraction, answers that require Ordering of numerical values, Counting and Other occurrences of simple mathematical operations 
An example can exhibit multiple forms of reasoning  Notably, we do not annotate any of the categories mentioned above if the expected answer is directly stated in the passage  For example, if the question asks “How many total points were scored in the game?” and the passage contains a sentence similar to “The total score of the game was 51 points”, it does not require any reasoning, in which case we annotate it as Retrieval 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Knowledge
Worthwhile knowing is whether the information presented in the context is sufficient to answer the question, as there is an increase of benchmarks deliberately designed to probe a model's reliance on some sort of background knowledge BIBREF24  We seek to categorise the type of knowledge required  Similar to Wang2019, on the one hand we annotate the reliance on factual knowledge, that is (Geo)political/Legal, Cultural/Historic, Technical/Scientific and Other Domain Specific knowledge about the world that can be expressed as a set of facts  On the other hand, we denote Intuitive knowledge requirements, which is challenging to express as a set of facts, such as the knowledge that a parenthetic numerical expression next to a person's name in a biography usually denotes his life span 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Linguistic Complexity
Another dimension of interest is the evaluation of various linguistic capabilities of MRC models BIBREF25, BIBREF26, BIBREF27  We aim to establish which linguistic phenomena are probed by gold standards and to which degree  To that end, we draw inspiration from the annotation schema used by Wang2019, and adapt it around lexical semantics and syntax 
More specifically, we annotate features that introduce variance between the supporting facts and the question  With regard to lexical semantics, we focus on the use of redundant words that do not alter the meaning of a sentence for the task of retrieving the expected answer (Redundancy), requirements on the understanding of words' semantic fields (Lexical Entailment) and the use of Synonyms and Paraphrases with respect to the question wording  Furthermore we annotate cases where supporting facts contain Abbreviations of concepts introduced in the question (and vice versa) and when a Dative case substitutes the use of a preposition (e g  “I bought her a gift” vs “I bought a gift for her”)  Regarding syntax, we annotate changes from passive to active Voice, the substitution of a Genitive case with a preposition (e g  “of”) and changes from nominal to verbal style and vice versa (Nominalisation) 
We recognise features that add ambiguity to the supporting facts, for example when information is only expressed implicitly by using an Ellipsis  As opposed to redundant words, we annotate Restrictivity and Factivity modifiers, words and phrases whose presence does change the meaning of a sentence with regard to the expected answer, and occurrences of intra- or inter-sentence Coreference in supporting facts (that is relevant to the question)  Lastly, we mark ambiguous syntactic features, when their resolution is required in order to obtain the answer  Concretely, we mark argument collection with con- and disjunctions (Listing) and ambiguous Prepositions, Coordination Scope and Relative clauses/Adverbial phrases/Appositions 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Complexity
Finally, we want to approximate the presence of lexical cues that might simplify the reading required in order to arrive at the answer  Quantifying this allows for more reliable statements about and comparison of the complexity of gold standards, particularly regarding the evaluation of comprehension that goes beyond simple lexical matching  We propose the use of coarse metrics based on lexical overlap between question and context sentences  Intuitively, we aim to quantify how much supporting facts “stand out” from their surrounding passage context  This can be used as proxy for the capability to retrieve the answer BIBREF10  Specifically, we measure (i) the number of words jointly occurring in a question and a sentence, (ii) the length of the longest n-gram shared by question and sentence and (iii) whether a word or n-gram from the question uniquely appears in a sentence 
The resulting taxonomy of the framework is shown in Figure FIGREF10  The full catalogue of features, their description, detailed annotation guideline as well as illustrating examples can be found in Appendix  
Application of the Framework ::: Candidate Datasets
We select contemporary MRC benchmarks to represent all four commonly used problem definitions BIBREF15  In selecting relevant datasets, we do not consider those that are considered “solved”, i e  where the state of the art performance surpasses human performance, as is the case with SQuAD BIBREF28, BIBREF7  Concretely, we selected gold standards that fit our problem definition and were published in the years 2016 to 2019, have at least $(2019 - publication\ year) \times 20$ citations, and bucket them according to the answer selection styles as described in Section SECREF4 We randomly draw one from each bucket and add two randomly drawn datasets from the candidate pool  This leaves us with the datasets described in Table TABREF19  For a more detailed description, we refer to Appendix  
Application of the Framework ::: Annotation Task
We randomly select 50 distinct question, answer and passage triples from the publicly available development sets of the described datasets  Training, development and the (hidden) test set are drawn from the same distribution defined by the data collection method of the respective dataset  For those collections that contain multiple questions over a single passage, we ensure that we are sampling unique paragraphs in order to increase the variety of investigated texts 
The samples were annotated by the first author of this paper, using the proposed schema  In order to validate our findings, we further take 20% of the annotated samples and present them to a second annotator (second author)  Since at its core, the annotation is a multi-label task, we report the inter-annotator agreement by computing the (micro-averaged) F1 score, where we treat the first annotator's labels as gold  Table TABREF21 reports the agreement scores, the overall (micro) average F1 score of the annotations is 0 82, which means that on average, more than two thirds of the overall annotated labels were agreed on by both annotators  We deem this satisfactory, given the complexity of the annotation schema 
Application of the Framework ::: Qualitative Analysis
We present a concise view of the annotation results in Figure FIGREF23  The full annotation results can be found in Appendix   We centre our discussion around the following main points:
Application of the Framework ::: Qualitative Analysis ::: Linguistic Features
As observed in Figure FIGREF23 the gold standards feature a high degree of Redundancy, peaking at 76% of the annotated HotpotQA samples and synonyms and paraphrases (labelled Synonym), with ReCoRd samples containing 58% of them, likely to be attributed to the elaborating type of discourse of the dataset sources (encyclopedia and newswire)  This is, however, not surprising, as it is fairly well understood in the literature that current state-of-the-art models perform well on distinguishing relevant words and phrases from redundant ones BIBREF32  Additionally, the representational capability of synonym relationships of word embeddings has been investigated and is well known BIBREF33  Finally, we observe the presence of syntactic features, such as ambiguous relative clauses, appositions and adverbial phrases, (RelAdvApp 40% in HotpotQA and ReCoRd) and those introducing variance, concretely switching between verbal and nominal styles (e g  Nominalisation 10% in HotpotQA) and from passive to active voice (Voice, 8% in HotpotQA) 
Syntactic features contributing to variety and ambiguity that we did not observe in our samples are the exploitation of verb symmetry, the use of dative and genitive cases or ambiguous prepositions and coordination scope (respectively Symmetry, Dative, Genitive, Prepositions, Scope)  Therefore we cannot establish whether models are capable of dealing with those features by evaluating them on those gold standards 
Application of the Framework ::: Qualitative Analysis ::: Factual Correctness
We identify three common sources that surface in different problems regarding an answer's factual correctness, as reported in Figure FIGREF23 and illustrate their instantiations in Table TABREF31:
Design Constraints: Choosing the task design and the data collection method introduces some constraints that lead to factually debatable examples  For example, a span might have been arbitrarily selected from multiple spans that potentially answer a question, but only a single continuous answer span per question is allowed by design, as observed in the NewsQA and MsMarco samples (32% and 34% examples annotated as Debatable with 16% and 53% thereof exhibiting arbitrary selection, respectively)  Sometimes, when additional passages are added after the annotation step, they can by chance contain passages that answer the question more precisely than the original span, as seen in HotpotQA (16% Debatable samples, 25% of them due to arbitrary selection)  In the case of MultiRC it appears to be inconsistent, whether multiple correct answer choices are expected to be correct in isolation or in conjunction (28% Debatable with 29% of them exhibiting this problem)  This might provide an explanation to its relatively weak human baseline performance of 84% F1 score BIBREF31 
Weak Quality assurance: When the (typically crowd-sourced) annotations are not appropriately validated, incorrect examples will find their way into the gold standards  This typically results in factually wrong expected answers (i e  when a more correct answer is present in the context) or a question is expected to be Unanswerable, but is actually answerable from the provided context  The latter is observed in MsMarco (83% of examples annotated as Wrong) and NewsQA, where 60% of the examples annotated as Wrong are Unanswerable with an answer present 
Arbitrary Precision: There appears to be no clear guideline on how precise the answer is expected to be, when the passage expresses the answer in varying granularities  We annotated instances as Debatable when the expected answer was not the most precise given the context (44% and 29% of Debatable instances in NewsQA and MultiRC, respectively) 
Application of the Framework ::: Qualitative Analysis ::: Semantics-altering grammatical modifiers
We took interest in whether any of the benchmarks contain what we call distracting lexical features (or distractors): grammatical modifiers that alter the semantics of a sentence for the final task of answering the given question while preserving a similar lexical form  An example of such features are cues for (double) Negation (e g , “no”, “not”), which when introduced in a sentence, reverse its meaning  Other examples include modifiers denoting Restrictivity, Factivity and Reasoning (such as Monotonicity and Conditional cues)  Examples of question-answer pairs containing a distractor are shown in Table FIGREF37 
We posit that the presence of such distractors would allow for evaluating reading comprehension beyond potential simple word matching  However, we observe no presence of such features in the benchmarks (beyond Negation in DROP, ReCoRd and HotpotQA, with 4%, 4% and 2% respectively)  This results in gold standards that clearly express the evidence required to obtain the answer, lacking more challenging, i e , distracting, sentences that can assess whether a model can truly understand meaning 
Application of the Framework ::: Qualitative Analysis ::: Other
In the Figure FIGREF23 we observe that Operational and Arithmetic reasoning moderately (6% to 8% combined) appears “in the wild”, i e  when not enforced by the data design as is the case with HotpotQA (80% Operations combined) or DROP (68% Arithmetic combined)  Causal reasoning is (exclusively) present in MultiRC (32%), whereas Temporal and Spatial reasoning requirements seem to not naturally emerge in gold standards  In ReCoRd, a fraction of 38% questions can only be answered By Exclusion of every other candidate, due to the design choice of allowing questions where the required information to answer them is not fully expressed in the accompanying paragraph 
Therefore, it is also a little surprising to observe that ReCoRd requires external resources with regard to knowledge, as seen in Figure FIGREF23  MultiRC requires technical or more precisely basic scientific knowledge (6% Technical/Scientific), as a portion of paragraphs is extracted from elementary school science textbooks BIBREF31  Other benchmarks moderately probe for factual knowledge (0% to 4% across all categories), while Intuitive knowledge is required to derive answers in each gold standard 
It is also worth pointing out, as done in Figure FIGREF23, that although MultiRC and MsMarco are not modelled as a span selection problem, their samples still contain 50% and 66% of answers that are directly taken from the context  DROP contains the biggest fraction of generated answers (60%), due to the requirement of arithmetic operations 
To conclude our analysis, we observe similar distributions of linguistic features and reasoning patterns, except where there are constraints enforced by dataset design, annotation guidelines or source text choice  Furthermore, careful consideration of design choices (such as single-span answers) is required, to avoid impairing the factual correctness of datasets, as pure crowd-worker agreement seems not sufficient in multiple cases 
Application of the Framework ::: Quantitative Results ::: Lexical overlap
We used the scores assigned by our proposed set of metrics (discussed in Section SECREF11 Dimensions of Interest: Complexity) to predict the supporting facts in the gold standard samples (that we included in our manual annotation)  Concretely, we used the following five features capturing lexical overlap: (i) the number of words occurring in sentence and question, (ii) the length of the longest n-gram shared by sentence and question, whether a (iii) uni- and (iv) bigram from the question is unique to a sentence, and (v) the sentence index, as input to a logistic regression classifier  We optimised on each sample leaving one example for evaluation  We compute the average Precision, Recall and F1 score by means of leave-one-out validation with every sample entry  The averaged results after 5 runs are reported in Table TABREF41 
We observe that even by using only our five features based lexical overlap, the simple logistic regression baseline is able to separate out the supporting facts from the context to a varying degree  This is in line with the lack of semantics-altering grammatical modifiers discussed in the qualitative analysis section above  The classifier performs best on DROP (66% F1) and MultiRC (40% F1), which means that lexical cues can considerably facilitate the search for the answer in those gold standards  On MultiRC, yadav2019quick come to a similar conclusion, by using a more sophisticated approach based on overlap between question, sentence and answer choices 
Surprisingly, the classifier is able to pick up a signal from supporting facts even on data that has been pruned against lexical overlap heuristics by populating the context with additional documents that have high overlap scores with the question  This results in significantly higher scores than when guessing randomly (HotpotQA 26% F1, and MsMarco 11% F1)  We observe similar results in the case the length of the question leaves few candidates to compute overlap with $6 3$ and $7 3$ tokens on average for MsMarco and NewsQA (26% F1), compared to $16 9$ tokens on average for the remaining four dataset samples 
Finally, it is worth mentioning that although the queries in ReCoRd are explicitly independent from the passage, the linear classifier is still capable of achieving 34% F1 score in predicting the supporting facts 
However, neural networks perform significantly better than our admittedly crude baseline (e g  66% F1 for supporting facts classification on HotpotQA BIBREF14), albeit utilising more training examples, and a richer sentence representation  This facts implies that those neural models are capable of solving more challenging problems than simple “text matching” as performed by the logistic regression baseline  However, they still circumvent actual reading comprehension as the respective gold standards are of limited suitability to evaluate this BIBREF34, BIBREF35  This suggests an exciting future research direction, that is categorising the scale between text matching and reading comprehension more precisely and respectively positioning state-of-the-art models thereon 
Related Work
Although not as prominent as the research on novel architecture, there has been steady progress in critically investigating the data and evaluation aspects of NLP and machine learning in general and MRC in particular 
Related Work ::: Adversarial Evaluation
The authors of the AddSent algorithm BIBREF11 show that MRC models trained and evaluated on the SQuAD dataset pay too little attention to details that might change the semantics of a sentence, and propose a crowd-sourcing based method to generate adversary examples to exploit that weakness  This method was further adapted to be fully automated BIBREF36 and applied to different gold standards BIBREF35  Our proposed approach differs in that we aim to provide qualitative justifications for those quantitatively measured issues 
Related Work ::: Sanity Baselines
Another line of research establishes sane baselines to provide more meaningful context to the raw performance scores of evaluated models  When removing integral parts of the task formulation such as question, the textual passage or parts thereof BIBREF37 or restricting model complexity by design in order to suppress some required form of reasoning BIBREF38, models are still able to perform comparably to the state-of-the-art  This raises concerns about the perceived benchmark complexity and is related to our work in a broader sense as one of our goals is to estimate the complexity of benchmarks 
Related Work ::: Benchmark evaluation in NLP
Beyond MRC, efforts similar to ours that pursue the goal of analysing the evaluation of established datasets exist in Natural Language Inference BIBREF13, BIBREF12  Their analyses reveal the existence of biases in training and evaluation data that can be approximated with simple majority-based heuristics  Because of these biases, trained models fail to extract the semantics that are required for the correct inference  Furthermore, a fair share of work was done to reveal gender bias in coreference resolution datasets and models BIBREF39, BIBREF40, BIBREF41 
Related Work ::: Annotation Taxonomies
Finally, related to our framework are works that introduce annotation categories for gold standards evaluation  Concretely, we build our annotation framework around linguistic features that were introduced in the GLUE suite BIBREF42 and the reasoning categories introduced in the WorldTree dataset BIBREF19  A qualitative analysis complementary to ours, with focus on the unanswerability patterns in datasets that feature unanswerable questions was done by Yatskar2019 
Conclusion
In this paper, we introduce a novel framework to characterise machine reading comprehension gold standards  This framework has potential applications when comparing different gold standards, considering the design choices for a new gold standard and performing qualitative error analyses for a proposed approach 
Furthermore we applied the framework to analyse popular state-of-the-art gold standards for machine reading comprehension: We reveal issues with their factual correctness, show the presence of lexical cues and we observe that semantics-altering grammatical modifiers are missing in all of the investigated gold standards  Studying how to introduce those modifiers into gold standards and observing whether state-of-the-art MRC models are capable of performing reading comprehension on text containing them, is a future research goal 
A future line of research is to extend the framework to be able to identify the different types of exploitable cues such as question or entity typing and concrete overlap patterns  This will allow the framework to serve as an interpretable estimate of reading comprehension complexity of gold standards  Finally, investigating gold standards under this framework where MRC models outperform the human baseline (e g  SQuAD) will contribute to a deeper understanding of the seemingly superb performance of deep learning approaches on them 
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

ground_truth: The resulting taxonomy of the framework is shown in Figure FIGREF10

FIGREF10

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 2:  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
Chinese word segmentation (CWS) is a task for Chinese natural language process to delimit word boundary  CWS is a basic and essential task for Chinese which is written without explicit word delimiters and different from alphabetical languages like English  BIBREF0 treats Chinese word segmentation (CWS) as a sequence labeling task with character position tags, which is followed by BIBREF1, BIBREF2, BIBREF3  Traditional CWS models depend on the design of features heavily which effects the performance of model  To minimize the effort in feature engineering, some CWS models BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11 are developed following neural network architecture for sequence labeling tasks BIBREF12  Neural CWS models perform strong ability of feature representation, employing unigram and bigram character embedding as input and approach good performance 
The CWS task is often modeled as one graph model based on a scoring model that means it is composed of two parts, one part is an encoder which is used to generate the representation of characters from the input sequence, the other part is a decoder which performs segmentation according to the encoder scoring  Table TABREF1 summarizes typical CWS models according to their decoding ways for both traditional and neural models  Markov models such as BIBREF13 and BIBREF4 depend on the maximum entropy model or maximum entropy Markov model both with a Viterbi decoder  Besides, conditional random field (CRF) or Semi-CRF for sequence labeling has been used for both traditional and neural models though with different representations BIBREF2, BIBREF15, BIBREF10, BIBREF17, BIBREF18  Generally speaking, the major difference between traditional and neural network models is about the way to represent input sentences 
Recent works about neural CWS which focus on benchmark dataset, namely SIGHAN Bakeoff BIBREF21, may be put into the following three categories roughly 
Encoder  Practice in various natural language processing tasks has been shown that effective representation is essential to the performance improvement  Thus for better CWS, it is crucial to encode the input character, word or sentence into effective representation  Table TABREF2 summarizes regular feature sets for typical CWS models including ours as well  The building blocks that encoders use include recurrent neural network (RNN) and convolutional neural network (CNN), and long-term memory network (LSTM) 
Graph model  As CWS is a kind of structure learning task, the graph model determines which type of decoder should be adopted for segmentation, also it may limit the capability of defining feature, as shown in Table 2, not all graph models can support the word features  Thus recent work focused on finding more general or flexible graph model to make model learn the representation of segmentation more effective as BIBREF9, BIBREF11 
External data and pre-trained embedding  Whereas both encoder and graph model are about exploring a way to get better performance only by improving the model strength itself  Using external resource such as pre-trained embeddings or language representation is an alternative for the same purpose BIBREF22, BIBREF23  SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21  In this work, we will focus on the closed test setting by finding a better model design for further CWS performance improvement 
Shown in Table TABREF1, different decoders have particular decoding algorithms to match the respective CWS models  Markov models and CRF-based models often use Viterbi decoders with polynomial time complexity  In general graph model, search space may be too large for model to search  Thus it forces graph models to use an approximate beam search strategy  Beam search algorithm has a kind low-order polynomial time complexity  Especially, when beam width $b$=1, the beam search algorithm will reduce to greedy algorithm with a better time complexity $O(Mn)$ against the general beam search time complexity $O(Mnb^2)$, where $n$ is the number of units in one sentences, $M$ is a constant representing the model complexity  Greedy decoding algorithm can bring the fastest speed of decoding while it is not easy to guarantee the precision of decoding when the encoder is not strong enough 
In this paper, we focus on more effective encoder design which is capable of offering fast and accurate Chinese word segmentation with only unigram feature and greedy decoding  Our proposed encoder will only consist of attention mechanisms as building blocks but nothing else  Motivated by the Transformer BIBREF24 and its strength of capturing long-range dependencies of input sentences, we use a self-attention network to generate the representation of input which makes the model encode sentences at once without feeding input iteratively  Considering the weakness of the Transformer to model relative and absolute position information directly BIBREF25 and the importance of localness information, position information and directional information for CWS, we further improve the architecture of standard multi-head self-attention of the Transformer with a directional Gaussian mask and get a variant called Gaussian-masked directional multi-head attention  Based on the newly improved attention mechanism, we expand the encoder of the Transformer to capture different directional information  With our powerful encoder, our model uses only simple unigram features to generate representation of sentences 
For decoder which directly performs the segmentation, we use the bi-affinal attention scorer, which has been used in dependency parsing BIBREF26 and semantic role labeling BIBREF27, to implement greedy decoding on finding the boundaries of words  In our proposed model, greedy decoding ensures a fast segmentation while powerful encoder design ensures a good enough segmentation performance even working with greedy decoder together  Our model will be strictly evaluated on benchmark datasets from SIGHAN Bakeoff shared task on CWS in terms of closed test setting, and the experimental results show that our proposed model achieves new state-of-the-art 
The technical contributions of this paper can be summarized as follows 
We propose a CWS model with only attention structure  The encoder and decoder are both based on attention structure 
With a powerful enough encoder, we for the first time show that unigram (character) featues can help yield strong performance instead of diverse $n$-gram (character and word) features in most of previous work 
To capture the representation of localness information and directional information, we propose a variant of directional multi-head self-attention to further enhance the state-of-the-art Transformer encoder 
Models
The CWS task is often modelled as one graph model based on an encoder-based scoring model  The model for CWS task is composed of an encoder to represent the input and a decoder based on the encoder to perform actual segmentation  Figure FIGREF6 is the architecture of our model  The model feeds sentence into encoder  Embedding captures the vector $e=(e_1,   ,e_n)$ of the input character sequences of $c=(c_1,   ,c_n)$  The encoder maps vector sequences of $ {e}=(e_1,  ,e_n)$ to two sequences of vector which are $ {v^b}=(v_1^b,   ,v_n^b)$ and ${v^f}=(v_1^f,   v_n^f)$ as the representation of sentences  With $v^b$ and $v^f$, the bi-affinal scorer calculates the probability of each segmentation gaps and predicts the word boundaries of input  Similar as the Transformer, the encoder is an attention network with stacked self-attention and point-wise, fully connected layers while our encoder includes three independent directional encoders 
Models ::: Encoder Stacks
In the Transformer, the encoder is composed of a stack of N identical layers and each layer has one multi-head self-attention layer and one position-wise fully connected feed-forward layer  One residual connection is around two sub-layers and followed by layer normalization BIBREF24  This architecture provides the Transformer a good ability to generate representation of sentence 
With the variant of multi-head self-attention, we design a Gaussian-masked directional encoder to capture representation of different directions to improve the ability of capturing the localness information and position information for the importance of adjacent characters  One unidirectional encoder can capture information of one particular direction 
For CWS tasks, one gap of characters, which is from a word boundary, can divide one sequence into two parts, one part in front of the gap and one part in the rear of it  The forward encoder and backward encoder are used to capture information of two directions which correspond to two parts divided by the gap 
One central encoder is paralleled with forward and backward encoders to capture the information of entire sentences  The central encoder is a special directional encoder for forward and backward information of sentences  The central encoder can fuse the information and enable the encoder to capture the global information 
The encoder outputs one forward information and one backward information of each positions  The representation of sentence generated by center encoder will be added to these information directly:
where $v^{b}=(v^b_1,   ,v^b_n)$ is the backward information, $v^{f}=(v^f_1,   ,v^f_n)$ is the forward information, $r^{b}=(r^b_1,   ,r^b_n)$ is the output of backward encoder, $r^{c}=(r^c_1,   ,r^c_n)$ is the output of center encoder and $r^{f}=(r^f_1,   ,r^f_n)$ is the output of forward encoder 
Models ::: Gaussian-Masked Directional Multi-Head Attention
Similar as scaled dot-product attention BIBREF24, Gaussian-masked directional attention can be described as a function to map queries and key-value pairs to the representation of input  Here queries, keys and values are all vectors  Standard scaled dot-product attention is calculated by dotting query $Q$ with all keys $K$, dividing each values by $\sqrt{d_k}$, where $\sqrt{d_k}$ is the dimension of keys, and apply a softmax function to generate the weights in the attention:
Different from scaled dot-product attention, Gaussian-masked directional attention expects to pay attention to the adjacent characters of each positions and cast the localness relationship between characters as a fix Gaussian weight for attention  We assume that the Gaussian weight only relys on the distance between characters 
Firstly we introduce the Gaussian weight matrix $G$ which presents the localness relationship between each two characters:
where $g_{ij}$ is the Gaussian weight between character $i$ and $j$, $dis_{ij}$ is the distance between character $i$ and $j$, $\Phi (x)$ is the cumulative distribution function of Gaussian, $\sigma $ is the standard deviation of Gaussian function and it is a hyperparameter in our method

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. What are strong baselines model is compared to?
Context 3:  Equation (DISPLAY_FORM13) can ensure the Gaussian weight equals 1 when $dis_{ij}$ is 0  The larger distance between charactersis, the smaller the weight is, which makes one character can affect its adjacent characters more compared with other characters 
To combine the Gaussian weight to the self-attention, we produce the Hadamard product of Gaussian weight matrix $G$ and the score matrix produced by $Q{K^{T}}$
where $AG$ is the Gaussian-masked attention  It ensures that the relationship between two characters with long distances is weaker than adjacent characters 
The scaled dot-product attention models the relationship between two characters without regard to their distances in one sequence  For CWS task, the weight between adjacent characters should be more important while it is hard for self-attention to achieve the effect explicitly because the self-attention cannot get the order of sentences directly  The Gaussian-masked attention adjusts the weight between characters and their adjacent character to a larger value which stands for the effect of adjacent characters 
For forward and backward encoder, the self-attention sublayer needs to use a triangular matrix mask to let the self-attention focus on different weights:
where $pos_i$ is the position of character $c_i$  The triangular matrix for forward and backward encode are:
$\left[ \begin{matrix} 1 & 0 & 0 & \cdots &0\\ 1 & 1 & 0 & \cdots &0\\ 1 & 1 & 1 & \cdots &0\\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 1 & 1 & 1 & \cdots & 1\\ \end{matrix} \right]$ $\left[ \begin{matrix} 1 & 1 & 1 & \cdots &1 \\ 0 & 1 & 1 & \cdots &1 \\ 0 & 0& 1 & \cdots &1 \\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 0 & 0 & 0 & \cdots & 1\\ \end{matrix}\right]$
Similar as BIBREF24, we use multi-head attention to capture information from different dimension positions as Figure FIGREF16 and get Gaussian-masked directional multi-head attention  With multi-head attention architecture, the representation of input can be captured by
where $MH$ is the Gaussian-masked multi-head attention, ${W_i^q, W_i^k,W_i^v} \in \mathbb {R}^{d_k \times d_h}$ is the parameter matrices to generate heads, $d_k$ is the dimension of model and $d_h$ is the dimension of one head 
Models ::: Bi-affinal Attention Scorer
Regarding word boundaries as gaps between any adjacent words converts the character labeling task to the gap labeling task  Different from character labeling task, gap labeling task requires information of two adjacent characters  The relationship between adjacent characters can be represented as the type of gap  The characteristic of word boundaries makes bi-affine attention an appropriate scorer for CWS task 
Bi-affinal attention scorer is the component that we use to label the gap  Bi-affinal attention is developed from bilinear attention which has been used in dependency parsing BIBREF26 and SRL BIBREF27  The distribution of labels in a labeling task is often uneven which makes the output layer often include a fixed bias term for the prior probability of different labels BIBREF27  Bi-affine attention uses bias terms to alleviate the burden of the fixed bias term and get the prior probability which makes it different from bilinear attention  The distribution of the gap is uneven that is similar as other labeling task which fits bi-affine 
Bi-affinal attention scorer labels the target depending on information of independent unit and the joint information of two units  In bi-affinal attention, the score $s_{ij}$ of characters $c_i$ and $c_j$ $(i < j)$ is calculated by:
where $v_i^f$ is the forward information of $c_i$ and $v_i^b$ is the backward information of $c_j$  In Equation (DISPLAY_FORM21), $W$, $U$ and $b$ are all parameters that can be updated in training  $W$ is a matrix with shape $(d_i \times N\times d_j)$ and $U$ is a $(N\times (d_i + d_j))$ matrix where $d_i$ is the dimension of vector $v_i^f$ and $N$ is the number of labels 
In our model, the biaffine scorer uses the forward information of character in front of the gap and the backward information of the character behind the gap to distinguish the position of characters  Figure FIGREF22 is an example of labeling gap  The method of using biaffine scorer ensures that the boundaries of words can be determined by adjacent characters with different directional information  The score vector of the gap is formed by the probability of being a boundary of word  Further, the model generates all boundaries using activation function in a greedy decoding way 
Experiments ::: Experimental Settings ::: Data
We train and evaluate our model on datasets from SIGHAN Bakeoff 2005 BIBREF21 which has four datasets, PKU, MSR, AS and CITYU  Table TABREF23 shows the statistics of train data  We use F-score to evaluate CWS models  To train model with pre-trained embeddings in AS and CITYU, we use OpenCC to transfer data from traditional Chinese to simplified Chinese 
Experiments ::: Experimental Settings ::: Pre-trained Embedding
We only use unigram feature so we only trained character embeddings  Our pre-trained embedding are pre-trained on Chinese Wikipedia corpus by word2vec BIBREF29 toolkit  The corpus used for pre-trained embedding is all transferred to simplified Chinese and not segmented  On closed test, we use embeddings initialized randomly 
Experiments ::: Experimental Settings ::: Hyperparameters
For different datasets, we use two kinds of hyperparameters which are presented in Table TABREF24  We use hyperparameters in Table TABREF24 for small corpora (PKU and CITYU) and normal corpora (MSR and AS)  We set the standard deviation of Gaussian function in Equation (DISPLAY_FORM13) to 2  Each training batch contains sentences with at most 4096 tokens 
Experiments ::: Experimental Settings ::: Optimizer
To train our model, we use the Adam BIBREF30 optimizer with $\beta _1=0 9$, $\beta _2=0 98$ and $\epsilon =10^{-9}$  The learning rate schedule is the same as BIBREF24:
where $d$ is the dimension of embeddings, $step$ is the step number of training and $warmup_step$ is the step number of warmup  When the number of steps is smaller than the step of warmup, the learning rate increases linearly and then decreases 
Experiments ::: Hardware and Implements
We trained our models on a single CPU (Intel i7-5960X) with an nVidia 1080 Ti GPU  We implement our model in Python with Pytorch 1 0 
Experiments ::: Results
Tables TABREF25 and TABREF26 reports the performance of recent models and ours in terms of closed test setting  Without the assistance of unsupervised segmentation features userd in BIBREF20, our model outperforms all the other models in MSR and AS except BIBREF18 and get comparable performance in PKU and CITYU  Note that all the other models for this comparison adopt various $n$-gram features while only our model takes unigram ones 
With unsupervised segmentation features introduced by BIBREF20, our model gets a higher result  Specially, the results in MSR and AS achieve new state-of-the-art and approaching previous state-of-the-art in CITYU and PKU  The unsupervised segmentation features are derived from the given training dataset, thus using them does not violate the rule of closed test of SIGHAN Bakeoff 
Table TABREF36 compares our model and recent neural models in terms of open test setting in which any external resources, especially pre-trained embeddings or language models can be used  In MSR and AS, our model gets a comparable result while our results in CITYU and PKU are not remarkable 
However, it is well known that it is always hard to compare models when using open test setting, especially with pre-trained embedding  Not all models may use the same method and data to pre-train  Though pre-trained embedding or language model can improve the performance, the performance improvement itself may be from multiple sources  It often that there is a success of pre-trained embedding to improve the performance, while it cannot prove that the model is better 
Compared with other LSTM models, our model performs better in AS and MSR than in CITYU and PKU  Considering the scale of different corpora, we believe that the size of corpus affects our model and the larger size is, the better model performs  For small corpus, the model tends to be overfitting 
Tables TABREF25 and TABREF26 also show the decoding time in different datasets  Our model finishes the segmentation with the least decoding time in all four datasets, thanks to the architecture of model which only takes attention mechanism as basic block 
Related Work ::: Chinese Word Segmentation
CWS is a task for Chinese natural language process to delimit word boundary  BIBREF0 for the first time formulize CWS as a sequence labeling task  BIBREF3 show that different character tag sets can make essential impact for CWS  BIBREF2 use CRFs as a model for CWS, achieving new state-of-the-art  Works of statistical CWS has built the basis for neural CWS 
Neural word segmentation has been widely used to minimize the efforts in feature engineering which was important in statistical CWS  BIBREF4 introduce the neural model with sliding-window based sequence labeling  BIBREF6 propose a gated recursive neural network (GRNN) for CWS to incorporate complicated combination of contextual character and n-gram features  BIBREF7 use LSTM to learn long distance information  BIBREF9 propose a neural framework that eliminates context windows and utilize complete segmentation history  BIBREF33 explore a joint model that performs segmentation, POS-Tagging and chunking simultaneously  BIBREF34 propose a feature-enriched neural model for joint CWS and part-of-speech tagging  BIBREF35 present a joint model to enhance the segmentation of Chinese microtext by performing CWS and informal word detection simultaneously  BIBREF17 propose a character-based convolutional neural model to capture $n$-gram features automatically and an effective approach to incorporate word embeddings  BIBREF11 improve the model in BIBREF9 and propose a greedy neural word segmenter with balanced word and character embedding inputs  BIBREF23 propose a novel neural network model to incorporate unlabeled and partially-labeled data  BIBREF36 propose two methods that extend the Bi-LSTM to perform incorporating dictionaries into neural networks for CWS  BIBREF37 propose Switch-LSTMs to segment words and provided a more flexible solution for multi-criteria CWS which is easy to transfer the learned knowledge to new criteria 
Related Work ::: Transformer
Transformer BIBREF24 is an attention-based neural machine translation model  The Transformer is one kind of self-attention networks (SANs) which is proposed in BIBREF38  Encoder of the Transformer consists of one self-attention layer and a position-wise feed-forward layer  Decoder of the Transformer contains one self-attention layer, one encoder-decoder attention layer and one position-wise feed-forward layer  The Transformer uses residual connections around the sublayers and then followed by a layer normalization layer 
Scaled dot-product attention is the key component in the Transformer  The input of attention contains queries, keys, and values of input sequences  The attention is generated using queries and keys like Equation (DISPLAY_FORM11)  Structure of scaled dot-product attention allows the self-attention layer generate the representation of sentences at once and contain the information of the sentence which is different from RNN that process characters of sentences one by one  Standard self-attention is similar as Gaussian-masked direction attention while it does not have directional mask and gaussian mask  BIBREF24 also propose multi-head attention which is better to generate representation of sentence by dividing queries, keys and values to different heads and get information from different subspaces 
Conclusion
In this paper, we propose an attention mechanism only based Chinese word segmentation model  Our model uses self-attention from the Transformer encoder to take sequence input and bi-affine attention scorer to predict the label of gaps  To improve the ability of capturing the localness and directional information of self-attention based encoder, we propose a variant of self-attention called Gaussian-masked directional multi-head attention to replace the standard self-attention  We also extend the Transformer encoder to capture directional features  Our model uses only unigram features instead of multiple $n$-gram features in previous work  Our model is evaluated on standard benchmark dataset, SIGHAN Bakeoff 2005, which shows not only our model performs segmentation faster than any previous models but also gives new higher or comparable segmentation performance against previous state-of-the-art models 

ground_truth: Baseline models are:
- Chen et al., 2015a
- Chen et al., 2015b
- Liu et al., 2016
- Cai and Zhao, 2016
- Cai et al., 2017
- Zhou et al., 2017
- Ma et al., 2018
- Wang et al., 2019