z-uo/qasper-squad huggingface dataset:
Paper: Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
In a long conversation, there exists a tendency of semantically related words, or phrases reoccur across sentences, or there exists topical coherence. Existing speech recognition systems are built at individual, isolated utterance level in order to make building systems computationally feasible. However, this may lose important conversational context information. There have been many studies that have attempted to inject a longer context information BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , all of these models are developed on text data for language modeling task.
Question: How are sentence embeddings incorporated into the speech recognition system?
Answer: BERT generates sentence embeddings that represent words in context. These sentence embeddings are merged into a single conversational-context vector that is used to calculate a gated embedding and is later combined with the output of the decoder h to provide the gated activations for the next hidden layer.
Paper: Supervised and Unsupervised Transfer Learning for Question Answering
One of the most important characteristics of an intelligent system is to understand stories like humans do. A story is a sequence of sentences, and can be in the form of plain text BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 or spoken content BIBREF0 , where the latter usually requires the spoken content to be first transcribed into text by automatic speech recognition (ASR), and the model will subsequently process the ASR output. To evaluate the extent of the model's understanding of the story, it is asked to answer questions about the story. Such a task is referred to as question answering (QA), and has been a long-standing yet challenging problem in natural language processing (NLP).
Question: How different is the dataset size of source and target?
Answer: the training dataset is large while the target dataset is usually much smaller
Paper: Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities
Knowledge about entities is essential for understanding human language. This knowledge can be attributional (e.g., canFly, isEdible), type-based (e.g., isFood, isPolitician, isDisease) or relational (e.g, marriedTo, bornIn). Knowledge bases (KBs) are designed to store this information in a structured way, so that it can be queried easily. Examples of such KBs are Freebase BIBREF3 , Wikipedia, Google knowledge graph and YAGO BIBREF4 . For automatic updating and completing the entity knowledge, text resources such as news, user forums, textbooks or any other data in the form of text are important sources. Therefore, information extraction methods have been introduced to extract knowledge about entities from text. In this paper, we focus on the extraction of entity types, i.e., assigning types to – or typing – entities. Type information can help extraction of relations by applying constraints on relation arguments.
Question: How do you find the entity descriptions?
Paper: Careful Selection of Knowledge to solve Open Book Question Answering
Natural language based question answering (NLQA) not only involves linguistic understanding, but often involves reasoning with various kinds of knowledge. In recent years, many NLQA datasets and challenges have been proposed, for example, SQuAD BIBREF0 , TriviaQA BIBREF1 and MultiRC BIBREF2 , and each of them have their own focus, sometimes by design and other times by virtue of their development methodology. Many of these datasets and challenges try to mimic human question answering settings. One such setting is open book question answering where humans are asked to answer questions in a setup where they can refer to books and other materials related to their questions. In such a setting, the focus is not on memorization but, as mentioned in BIBREF3 , on “deeper understanding of the materials and its application to new situations BIBREF4 , BIBREF5 .” In BIBREF3 , they propose the OpenBookQA dataset mimicking this setting.
Question: How is OpenBookQA different from other natural language QA?
Paper: Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Business documents broadly characterize a large class of documents that are central to the operation of business. These include legal contracts, purchase orders, financial statements, regulatory filings, and more. Such documents have a number of characteristics that set them apart from the types of texts that most NLP techniques today are designed to process (Wikipedia articles, news stories, web pages, etc.): They are heterogeneous and frequently contain a mix of both free text as well as semi-structured elements (tables, headings, etc.). They are, by definition, domain specific, often with vocabulary, phrases, and linguistic structures (e.g., legal boilerplate and terms of art) that are rarely seen in general natural language corpora.
Question: At what text unit/level were documents processed?
Paper: Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Within the broad space of business documents, we have decided to focus on two specific types: regulatory filings and property lease agreements. While our approach is not language specific, all our work is conducted on Chinese documents. In this section, we first describe these documents and our corpora, our sequence labeling model, and finally our evaluation approach.
Question: What evaluation metric were used for presenting results?
Paper: Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Regulatory Filings. We focused on a specific type of filing: disclosures of pledges by shareholders when their shares are offered up for collateral. These are publicly accessible and were gathered from the database of a stock exchange in China. We observe that most of these announcements are fairly formulaic, likely generated by templates. However, we treated them all as natural language text and did not exploit this observation; for example, we made no explicit attempt to induce template structure or apply clustering—although such techniques would likely improve extraction accuracy. In total, we collected and manually annotated 150 filings, which were divided into training, validation, and test sets with a 6:2:2 split. Our test corpus comprises 30 regulatory filings. Table TABREF6 enumerates the seven content elements that we extract.
Question: Was the structure of regulatory filings exploited when training the model?
Paper: Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
An obvious approach to content element extraction is to formulate the problem as a sequence labeling task. Prior to the advent of neural networks, Conditional Random Fields (CRFs) BIBREF4, BIBREF5 represented the most popular approach to this task. Starting from a few years ago, neural networks have become the dominant approach, starting with RNNs BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11. Most recently, deep transformer-based models such as BERT represent the state of the art in this task BIBREF1, BIBREF12, BIBREF13 . We adopt the sequence labeling approach of BIBREF1, based on annotations of our corpus using a standard BIO tagging scheme with respect to the content elements we are interested in.
Question: What type of documents are supported by the annotation platform?
Answer: Variety of formats supported (PDF, Word...), user can define content elements of document
Paper: Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection
Disinformation presents a serious threat to society, as the proliferation of fake news can have a significant impact on an individual's perception of reality. Fake news is a claim or story that is fabricated, with the intention to deceive, often for a secondary motive such as economic or political gain BIBREF0. In the age of digital news and social media, fake news can spread rapidly, impacting large amounts of people in a short period of time BIBREF1. To mitigate the negative impact of fake news on society, various organizations now employ personnel to verify dubious claims through a manual fact-checking procedure, however, this process is very laborious. With a fast-paced modern news cycle, many journalists and fact-checkers are under increased stress to be more efficient in their daily work. To assist in this process, automated fact-checking has been proposed as a potential solution BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6.
Question: What are the state-of-the-art models for the task?
Paper: Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Semantic composition plays an important role in sentiment analysis of phrases and sentences. This includes detecting the scope and impact of negation in reversing a sentiment's polarity, as well as quantifying the influence of modifiers, such as degree adverbs and intensifiers, in rescaling the sentiment's intensity BIBREF0 .
Question: Which datasets are used for evaluation?
Paper: Multi-task learning to improve natural language understanding
One of the main challenges in building a Natural Language Understanding (NLU) component for a specific task is the necessary human effort to encode the task's specific knowledge. In traditional NLU components, this was done by creating hand-written rules. In today's state-of-the-art NLU components, significant amounts of human effort have to be used for collecting the training data. For example, when building an NLU component for airplane travel information, there are a lot of possibilities to express the situation that someone wants to book a flight from New York to Pittsburgh. In order to build a system, we need to have seen many of them in the training data. Although more and more data has been collected and datasets with this data have been published BIBREF0 , the datasets often consist of data from another domain, which is needed for a certain NLU component.
Question: What are the strong baselines you have?
Answer: optimize single task with no synthetic data
Paper: Inferring the size of the causal universe: features and fusion of causal attribution networks
In this work we compare causal attribution networks derived from three datasets. A causal attribution dataset is a collection of text pairs that reflect cause-effect relationships proposed by humans (for example, “virus causes sickness”). These written statements identify the nodes of the network (see also our graph fusion algorithm for dealing with semantically equivalent statements) while cause-effect relationships form the directed edges (“virus” $\rightarrow $ “sickness”) of the causal attribution network.
Question: What are causal attribution networks?
Answer: networks where nodes represent causes and effects, and directed edges represent cause-effect relationships proposed by humans
Paper: Why Do Urban Legends Go Viral?
Urban legends are a genre of modern folklore consisting of stories told as true – and plausible enough to be believed – about some rare and exceptional events that supposedly happened to a real person or in a real place.
Question: How accurate is their predictive model?
Paper: An efficient automated data analytics approach to large scale computational comparative linguistics
The need to uncover presumed underlying linguistic evolutionary principles and analyse correlation between world's languages has entailed this research. For centuries people have been speculating about the origins of language, however this subject is still obscure. Non-automated linguistic analysis of language relationships has been complicated and very time-consuming. Consequently, this research aims to apply a computational approach to compare human languages. It is based on the phonetic representation of certain key words and concept. This comparison of word similarity aims to facilitate the grouping of languages and the analysis of the formation of genealogical relationship between languages.
Question: How large language sets are able to be explored using this approach?
Paper: A Dictionary-based Approach to Racism Detection in Dutch Social Media
1.1em
Question: how did they ask if a tweet was racist?
Answer: if it includes  negative utterances, negative generalizations and insults concerning ethnicity, nationality, religion and culture.
Paper: Neural Factor Graph Models for Cross-lingual Morphological Tagging
Morphological analysis (hajivc1998tagging, oflazer1994tagging, inter alia) is the task of predicting fine-grained annotations about the syntactic properties of tokens in a language such as part-of-speech, case, or tense. For instance, in Figure FIGREF2 , the given Portuguese sentence is labeled with the respective morphological tags such as Gender and its label value Masculine.
Question: What other cross-lingual approaches is the model compared to?
Paper: Neural Factor Graph Models for Cross-lingual Morphological Tagging
Formally, we define the problem of morphological analysis as the task of mapping a length- INLINEFORM0 string of tokens INLINEFORM1 into the target morphological tag sets for each token INLINEFORM2 . For the INLINEFORM3 th token, the target label INLINEFORM4 defines a set of tags (e.g. {Gender: Masc, Number: Sing, POS: Verb}). An annotation schema defines a set INLINEFORM5 of INLINEFORM6 possible tag types and with the INLINEFORM7 th type (e.g. Gender) defining its set of possible labels INLINEFORM8 (e.g. {Masc, Fem, Neu}) such that INLINEFORM9 . We must note that not all tags or attributes need to be specified for a token; usually, a subset of INLINEFORM10 is specified for a token and the remaining tags can be treated as mapping to a INLINEFORM11 value. Let INLINEFORM12 denote the set of all possible tag sets.
Question: What languages are explored?
Paper: That and There: Judging the Intent of Pointing Actions with Robotic Arms
Recent years have seen a rapid increase of robotic deployment, beyond traditional applications in cordoned-off workcells in factories, into new, more collaborative use-cases. For example, social robotics and service robotics have targeted scenarios like rehabilitation, where a robot operates in close proximity to a human. While industrial applications envision full autonomy, these collaborative scenarios involve interaction between robots and humans and require effective communication. For instance, a robot that is not able to reach an object may ask for a pick-and-place to be executed in the context of collaborative assembly. Or, in the context of a robotic assistant, a robot may ask for confirmation of a pick-and-place requested by a person.
Question: How many human subjects were used in the study?
Paper: Weakly-supervised Neural Semantic Parsing with a Generative Ranker
Semantic parsing is the task of converting natural language utterances into machine-understandable meaning representations or logical forms. The task has attracted much attention in the literature due to a wide range of applications ranging from question answering BIBREF0 , BIBREF1 to relation extraction BIBREF2 , goal-oriented dialog BIBREF3 , and instruction understanding BIBREF4 , BIBREF5 , BIBREF6 .
Question: How does the model compute the likelihood of executing to the correction semantic denotation?
Answer: By treating logical forms as a latent variable and training a discriminative log-linear model over logical form y given x.
Paper: Neural Machine Translation with Supervised Attention
Neural Machine Translation (NMT) has achieved great successes on machine translation tasks recently BIBREF0 , BIBREF1 . Generally, it relies on a recurrent neural network under the Encode-Decode framework: it firstly encodes a source sentence into context vectors and then generates its translation token-by-token, selecting from the target vocabulary. Among different variants of NMT, attention based NMT, which is the focus of this paper, is attracting increasing interests in the community BIBREF0 , BIBREF2 . One of its advantages is that it is able to dynamically make use of the encoded context through an attention mechanism thereby allowing the use of fewer hidden layers while still maintaining high levels of translation performance.
Question: Which conventional alignment models do they use as guidance?
Paper: Neural Machine Translation with Supervised Attention
Suppose INLINEFORM0 denotes a source sentence, INLINEFORM1 a target sentence. In addition, let INLINEFORM2 denote a prefix of INLINEFORM3 . Neural Machine Translation (NMT) directly maps a source sentence into a target under an encode-decode framework. In the encoding stage, it uses two bidirectional recurrent neural networks to encode INLINEFORM4 into a sequence of vectors INLINEFORM5 , with INLINEFORM6 representing the concatenation of two vectors for INLINEFORM7 source word from two directional RNNs. In the decoding stage, it generates the target translation from the conditional probability over the pair of sequences INLINEFORM8 and INLINEFORM9 via a recurrent neural network parametrized by INLINEFORM10 as follows: DISPLAYFORM0 
Question: Which dataset do they use?
Paper: Dover: A Method for Combining Diarization Outputs
Speaker diarization is the task of segmenting an audio recording in time, indexing each segment by speaker identity. In the standard version of the task BIBREF0, the goal is not to identify known speakers, but to co-index segments that are attributed to the same speaker; in other words, the task implies finding speaker boundaries and grouping segments that belong to the same speaker (including determining the number of distinct speakers). Often diarization is run, in parallel or in sequence, with speech recognition with the goal of achieving speaker-attributed speech-to-text transcription BIBREF1.
Question: On average, by how much do they reduce the diarization error?
Paper: Dover: A Method for Combining Diarization Outputs
The reason that combining diarization outputs in a ROVER-like manner is not straightforward is the complex structure of the task: a diarization system has to perform segmentation (finding speaker boundaries) and decisions about identity of speakers across segments. Where those functions are performed by specialized classifiers inside the diarization algorithm, ensemble methods could easily be used. For example, multiple speaker change detectors could vote on a consensus, or a speaker clustering algorithm could combine multiple acoustic embeddings to evaluate cluster similarity BIBREF7.
Question: Do they compare their algorithm to voting without weights?
Paper: Dover: A Method for Combining Diarization Outputs
Our algorithm maps the anonymous speaker labels from multiple diarization outputs into a common label space, and then performs a simple voting for each region of audio. A “region” for this purpose is a maximal segment delimited by any of the original speaker boundaries, from any of the input segmentations. The combined (or consensus) labeling is then obtained by stringing the majority labels for all regions together.
Question: How do they assign weights between votes in their DOVER algorithm?
Paper: Detecting Fake News with Capsule Neural Networks
Flexibility and ease of access to social media have resulted in the use of online channels for news access by a great number of people. For example, nearly two-thirds of American adults have access to news by online channels BIBREF0, BIBREF1. BIBREF2 also reported that social media and news consumption is significantly increased in Great Britain.
Question: What are state of the art methods authors compare their work with? 
Answer: ISOT dataset: LLVM
Liar dataset: Hybrid CNN and LSTM with attention
Paper: All Fingers are not Equal: Intensity of References in Scientific Articles
With more than one hundred thousand new scholarly articles being published each year, there is a rapid growth in the number of citations for the relevant scientific articles. In this context, we highlight the following interesting facts about the process of citing scientific articles: (i) the most commonly cited paper by Gerard Salton, titled “A Vector Space Model for Information Retrieval” (alleged to have been published in 1975) does not actually exist in reality BIBREF0 , (ii) the scientific authors read only 20% of the works they cite BIBREF1 , (iii) one third of the references in a paper are redundant and 40% are perfunctory BIBREF2 , (iv) 62.7% of the references could not be attributed a specific function (definition, tool etc.) BIBREF3 . Despite these facts, the existing bibliographic metrics consider that all citations are equally significant.
Question: What are the baselines model?
Paper: Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning
Code-switching has received a lot of attention from speech and computational linguistic communities especially on how to automatically recognize text from speech and understand the structure within it. This phenomenon is very common in bilingual and multilingual communities. For decades, linguists studied this phenomenon and found that speakers switch at certain points, not randomly and obeys several constraints which point to the code-switched position in an utterance BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . These hypotheses have been empirically proven by observing that bilinguals tend to code-switch intra-sententially at certain (morpho)-syntactic boundaries BIBREF5 . BIBREF1 defined the well-known theory that constraints the code-switch between a functional head and its complement is given the strong relationship between the two constituents, which corresponds to a hierarchical structure in terms of Part-of-Speech (POS) tags. BIBREF3 introduced Matrix-Language Model Framework for an intra-sentential case where the primary language is called Matrix Language and the second one called Embedded Language BIBREF2 . A language island was then introduced which is a constituent composed entirely of the language morphemes. From the Matrix-Language Frame Model, both matrix language (ML) island and embedded language (EL) islands are well-formed in their grammars and the EL islands are constrained under ML grammar BIBREF6 . BIBREF7 studied determiner–noun switches in Spanish–English bilinguals .
Question: What is the architecture of the model?
Paper: Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning
The earliest language modeling research on code-switching data was applying linguistic theories on computational modelings such as Inversion Constraints and Functional Head Constraints on Chinese-English code-switching data BIBREF9 , BIBREF10 . BIBREF11 built a bilingual language model which is trained by interpolating two monolingual language models with statistical machine translation (SMT) based text generation to generate artificial code-switching text. BIBREF12 , BIBREF13 introduced a class-based method using RNNLM for computing the posterior probability and added POS tags in the input. BIBREF14 explored the combination of brown word clusters, open class words, and clusters of open class word embeddings as hand-crafted features for improving the factored language model. In addition, BIBREF15 proposed a generative language modeling with explicit phrase structure. A method of tying input and output embedding helped to reduce the number of parameters in language model and improved the perplexity BIBREF16 .
Question: What languages are explored in the work?
Paper: Gender Bias in Neural Natural Language Processing
Natural language processing (NLP) with neural networks has grown in importance over the last few years. They provide state-of-the-art models for tasks like coreference resolution, language modeling, and machine translation BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . However, since these models are trained on human language texts, a natural question is whether they exhibit bias based on gender or other characteristics, and, if so, how should this bias be mitigated. This is the question that we address in this paper.
Question: What is the state-of-the-art neural coreference resolution model?