z-uo/qasper-squad huggingface dataset:
Paper: Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
Most studies on lexical acquisition typically focus on lexicons about objects BIBREF0 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . Many of these studies have not be able to address the lexical acquisition of words other than those related to objects, e.g., words about places.
Question: How do they evaluate how their model acquired words?
Paper: Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
The following studies have addressed lexical acquisition related to places. However, these studies could not utilize the learned language knowledge in other estimations such as the self-localization of a robot.
Question: Which method do they use for word segmentation?
Paper: Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
We propose nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates a nonparametric morphological analyzer for the lattice BIBREF22 , i.e., latticelm, a spatial clustering method, and Monte Carlo localization (MCL) BIBREF23 .
Question: Does their model start with any prior knowledge of words?
Paper: MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
Misinformation and disinformation are two of the most pertinent and difficult challenges of the information age, exacerbated by the popularity of social media. In an effort to counter this, a significant amount of manual labour has been invested in fact checking claims, often collecting the results of these manual checks on fact checking portals or websites such as politifact.com or snopes.com. In a parallel development, researchers have recently started to view fact checking as a task that can be partially automated, using machine learning and NLP to automatically predict the veracity of claims. However, existing efforts either use small datasets consisting of naturally occurring claims (e.g. BIBREF0 , BIBREF1 ), or datasets consisting of artificially constructed claims such as FEVER BIBREF2 . While the latter offer valuable contributions to further automatic claim verification work, they cannot replace real-world datasets.
Question: What were the baselines?
Paper: MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
Over the past few years, a variety of mostly small datasets related to fact checking have been released. An overview over core datasets is given in Table TABREF4 , and a version of this table extended with the number of documents, source of annotations and SoA performances can be found in the appendix (Table TABREF1 ). The datasets can be grouped into four categories (I–IV). Category I contains datasets aimed at testing how well the veracity of a claim can be predicted using the claim alone, without context or evidence documents. Category II contains datasets bundled with documents related to each claim – either topically related to provide context, or serving as evidence. Those documents are, however, not annotated. Category III is for predicting veracity; they encourage retrieving evidence documents as part of their task description, but do not distribute them. Finally, category IV comprises datasets annotated for both veracity and stance. Thus, every document is annotated with a label indicating whether the document supports or denies the claim, or is unrelated to it. Additional labels can then be added to the datasets to better predict veracity, for instance by jointly training stance and veracity prediction models.
Question: What metadata is included?
Answer: besides claim, label and claim url, it also includes a claim ID, reason, category, speaker, checker, tags, claim entities, article title, publish data and claim date
Paper: MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
Fact checking methods partly depend on the type of dataset used. Methods only taking into account claims typically encode those with CNNs or RNNs BIBREF3 , BIBREF4 , and potentially encode metadata BIBREF3 in a similar way. Methods for small datasets often use hand-crafted features that are a mix of bag of word and other lexical features, e.g. LIWC, and then use those as input to a SVM or MLP BIBREF0 , BIBREF4 , BIBREF13 . Some use additional Twitter-specific features BIBREF26 . More involved methods taking into account evidence documents, often trained on larger datasets, consist of evidence identification and ranking following a neural model that measures the compatibility between claim and evidence BIBREF2 , BIBREF27 , BIBREF28 .
Question: How many expert journalists were there?
Paper: Learning Multilingual Word Embeddings Using Image-Text Data
Recent advances in learning distributed representations for words (i.e., word embeddings) have resulted in improvements across numerous natural language understanding tasks BIBREF0 , BIBREF1 . These methods use unlabeled text corpora to model the semantic content of words using their co-occurring context words. Key to this is the observation that semantically similar words have similar contexts BIBREF2 , thus leading to similar word embeddings. A limitation of these word embedding approaches is that they only produce monolingual embeddings. This is because word co-occurrences are very likely to be limited to being within language rather than across language in text corpora. Hence semantically similar words across languages are unlikely to have similar word embeddings.
Question: Do the images have multilingual annotations or monolingual ones?
Answer: monolingual
Paper: Learning Multilingual Word Embeddings Using Image-Text Data
Most work on producing multilingual embeddings has relied on crosslingual human-labeled data, such as bilingual lexicons BIBREF13 , BIBREF4 , BIBREF6 , BIBREF14 or parallel/aligned corpora BIBREF15 , BIBREF4 , BIBREF16 , BIBREF17 . These works are also largely bilingual due to either limitations of methods or the requirement for data that exists only for a few language pairs. Bilingual embeddings are less desirable because they do not leverage the relevant resources of other languages. For example, in learning bilingual embeddings for English and French, it may be useful to leverage resources in Spanish, since French and Spanish are closely related. Bilingual embeddings are also limited in their applications to just one language pair.
Question: Could you learn such embedding simply from the image annotations and without using visual information?
Paper: Learning Multilingual Word Embeddings Using Image-Text Data
We experiment using a dataset derived from Google Images search results. The dataset consists of queries and the corresponding image search results. For example, one (query, image) pair might be “cat with big ears” and an image of a cat. Each (query, image) pair also has a weight corresponding to a relevance score of the image for the query. The dataset includes 3 billion (query, image, weight) triples, with 900 million unique images and 220 million unique queries. The data was prepared by first taking the query-image set, filtering to remove any personally identifiable information and adult content, and tokenizing the remaining queries by replacing special characters with spaces and trimming extraneous whitespace. Rare tokens (those that do not appear in queries at least six times) are filtered out. Each token in each query is given a language tag based on the user-set home language of the user making the search on Google Images. For example, if the query “back pain” is made by a user with English as her home language, then the query is stored as “en:back en:pain”. The dataset includes queries in about 130 languages.
Question: How much important is the visual grounding in the learning of the multilingual representations?
Answer: performance is significantly degraded without pixel data
Paper: Generating Natural Language Inference Chains
The ability to determine entailment or contradiction between natural language text is essential for improving the performance in a wide range of natural language processing tasks. Recognizing Textual Entailment (RTE) is a task primarily designed to determine whether two natural language sentences are independent, contradictory or in an entailment relationship where the second sentence (the hypothesis) can be inferred from the first (the premise). Although systems that perform well in RTE could potentially be used to improve question answering, information extraction, text summarization and machine translation BIBREF0 , only in few of such downstream NLP tasks sentence-pairs are actually available. Usually, only a single source sentence (e.g. a question that needs to be answered or a source sentence that we want to translate) is present and models need to come up with their own hypotheses and commonsense knowledge inferences.
Question: How is the generative model evaluated?
Answer: Comparing BLEU score of model with and without attention
Paper: Deep Representation Learning for Clustering of Health Tweets
Social media plays an important role in health informatics and Twitter has been one of the most influential social media channel for mining population-level health insights BIBREF0 , BIBREF1 , BIBREF2 . These insights range from forecasting of influenza epidemics BIBREF3 to predicting adverse drug reactions BIBREF4 . A notable challenge due to the short length of Twitter messages is categorization of tweets into topics in a supervised manner, i.e., topic classification, as well as in an unsupervised manner, i.e., clustering.
Question: How do they evaluate their method?
Paper: Deep Representation Learning for Clustering of Health Tweets
Devising efficient representations of tweets, i.e., features, for performing clustering has been studied extensively. Most frequently used features for representing the text in tweets as numerical vectors are bag-of-words (BoWs) and term frequency-inverse document frequency (tf-idf) features BIBREF17 , BIBREF9 , BIBREF10 , BIBREF18 , BIBREF19 . Both of these feature extraction methods are based on word occurrence counts and eventually, result in a sparse (most elements being zero) document-term matrix. Proposed algorithms for clustering tweets into topics include variants of hierarchical, density-based and centroid-based clustering methods; k-means algorithm being the most frequently used one BIBREF9 , BIBREF19 , BIBREF20 .
Question: What is an example of a health-related tweet?
Answer: The health benefits of alcohol consumption are more limited than previously thought, researchers say
Paper: SOC: hunting the underground inside story of the ethereum Social-network Opinion and Comment
Since Satoshi Nakamoto published the article "Bitcoin: A Peer-to-Peer Electronic Cash System" in 2008 BIBREF0 , and after the official launch of Bitcoin in 2009, technologies such as blockchain and cryptocurrency have attracted attention from academia and industry. At present, the technologies have been applied to many fields such as medical science, economics, Internet of Things BIBREF1 . Since the launch of Ethereum (Next Generation Encryption Platform) BIBREF2 with smart contract function proposed by Vitalik Buterin in 2015, lots of attention has been obtained on its dedicated cryptocurrency Ether, smart contract, blockchain and its decentralized Ethereum Virtual Machine (EVM). The main reason is that its design method provides developers with the ability to develop Decentralized apps (Dapps), and thus obtain wider applications. A new application paradigm opens the door to many possibilities and opportunities.
Question: Was the introduced LSTM+CNN model trained on annotated data in a supervised fashion?
Paper: Offensive Language and Hate Speech Detection for Danish
Offensive language in user-generated content on online platforms and its implications has been gaining attention over the last couple of years. This interest is sparked by the fact that many of the online social media platforms have come under scrutiny on how this type of content should be detected and dealt with. It is, however, far from trivial to deal with this type of language directly due to the gigantic amount of user-generated content created every day. For this reason, automatic methods are required, using natural language processing (NLP) and machine learning techniques.
Question: What is the challenge for other language except English
Answer: not researched as much as English
Paper: Offensive Language and Hate Speech Detection for Danish
Offensive language varies greatly, ranging from simple profanity to much more severe types of language. One of the more troublesome types of language is hate speech and the presence of hate speech on social media platforms has been shown to be in correlation with hate crimes in real life settings BIBREF1 . It can be quite hard to distinguish between generally offensive language and hate speech as few universal definitions exist BIBREF2 . There does, however, seem to be a general consensus that hate speech can be defined as language that targets a group with the intent to be harmful or to cause social chaos. This targeting is usually done on the basis of some characteristics such as race, color, ethnicity, gender, sexual orientation, nationality or religion BIBREF3 . In section "Background" , hate speech is defined in more detail. Offensive language, on the other hand, is a more general category containing any type of profanity or insult. Hate speech can, therefore, be classified as a subset of offensive language. BIBREF0 propose guidelines for classifying offensive language as well as the type and the target of offensive language. These guidelines capture the characteristics of generally offensive language, hate speech and other types of targeted offensive language such as cyberbullying. However, despite offensive language detection being a burgeoning field, no dataset yet exists for Danish BIBREF4 despite this phenomenon being present BIBREF5 .
Question: How many categories of offensive language were there?
Answer: 3
Paper: Offensive Language and Hate Speech Detection for Danish
In this section we give a comprehensive overview of the structure of the task and describe the dataset provided in BIBREF0 . Our work adopts this framing of the offensive language phenomenon.
Question: How large was the dataset of Danish comments?
Paper: Offensive Language and Hate Speech Detection for Danish
Offensive content is broken into three sub-tasks to be able to effectively identify both the type and the target of the offensive posts. These three sub-tasks are chosen with the objective of being able to capture different types of offensive language, such as hate speech and cyberbullying (section "Background" ).
Question: Who were the annotators?
Paper: Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo
In 2019, Freedom in the World, a yearly survey produced by Freedom House that measures the degree of civil liberties and political rights in every nation, recorded the 13th consecutive year of decline in global freedom. This decline spans across long-standing democracies such as USA as well as authoritarian regimes such as China and Russia. “Democracy is in retreat. The offensive against freedom of expression is being supercharged by a new and more effective form of digital authoritarianism." According to the report, China is now exporting its model of comprehensive internet censorship and surveillance around the world, offering trainings, seminars, and even study trips as well as advanced equipment.
Question: Is is known whether Sina Weibo posts are censored by humans or some automatic classifier?
Paper: Multi-turn Inference Matching Network for Natural Language Inference
Natural Language Inference (NLI) is a crucial subtopic in Natural Language Processing (NLP). Most studies treat NLI as a classification problem, aiming at recognizing the relation types of hypothesis-premise sentence pairs, usually including “Entailment”, “Contradiction” and “Neutral”.
Question: Which matching features do they employ?
Answer: Matching features from matching sentences from various perspectives.
Paper: The Logoscope: a Semi-Automatic Tool for Detecting and Documenting French New Words
Like other areas of linguistics, the study of new words has benefited from the development of natural language processing techniques in the last twenty years. In this research area, the digital revolution has significantly changed the way to collect the data necessary for empirical research BIBREF0 , BIBREF1 : The traditional practice of collecting new words by reading texts (newspapers, literature, scientific and technical texts, etc.), has been supplemented by an automatisation of the collection process. In fact, today there are many computer tools capable of searching through large amounts of texts (often newspapers) in order to automatically detect the newly created words in various languages (German, Catalan, Spanish, English, French, etc.).
Question: How often are the newspaper websites crawled daily?
Paper: TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks
Recurrent neural network (RNN) based techniques such as language models are the most popular approaches for text generation. These RNN-based text generators rely on maximum likelihood estimation (MLE) solutions such as teacher forcing BIBREF0 (i.e. the model is trained to predict the next item given all previous observations); however, it is well-known in the literature that MLE is a simplistic objective for this complex NLP task BIBREF1 . MLE-based methods suffer from exposure bias BIBREF2 , which means that at training time the model is exposed to gold data only, but at test time it observes its own predictions.
Question: How much better in terms of JSD measure did their model perform?
Paper: TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks
Generative adversarial networks include two separate deep networks: a generator and a discriminator. The generator takes in a random variable, INLINEFORM0 following a distribution INLINEFORM1 and attempt to map it to the data distribution INLINEFORM2 . The output distribution of the generator is expected to converge to the data distribution during the training. On the other hand, the discriminator is expected to discern real samples from generated ones by outputting zeros and ones, respectively. During training, the generator and discriminator generate samples and classify them, respectively by adversarially affecting the performance of each other. In this regard, an adversarial loss function is employed for training BIBREF16 : DISPLAYFORM0 
Question: What does the Jensen-Shannon distance measure?
Paper: Automating Political Bias Prediction
Modern media generate a large amount of content at an ever increasing rate. Keeping an unbiased view on what media report on requires to understand the political bias of texts. In many cases it is obvious which political bias an author has. In other cases some expertise is required to judge the political bias of a text. When dealing with large amounts of text however there are simply not enough experts to examine all possible sources and publications. Assistive technology can help in this context to try and obtain a more unbiased sample of information.
Question: Which countries and languages do the political speeches and manifestos come from?
Paper: Automating Political Bias Prediction
Throughout the last years automated content analyses for political texts have been conducted on a variety of text data sources (parliament data blogs, tweets, news articles, party manifestos) with a variety of methods, including sentiment analysis, stylistic analyses, standard bag-of-word (BOW) text feature classifiers and more advanced natural language processing tools. While a complete overview is beyond the scope of this work, the following paragraphs list similarities and differences between this study and previous work. For a more complete overview we refer the reader to BIBREF2 , BIBREF3 .
Question: Do changes in policies of the political actors account for all of the mistakes the model made?
Paper: Automating Political Bias Prediction
All experiments were run on publicly available data sets of german political texts and standard libraries for processing the text. The following sections describe the details of data acquisition and feature extraction.
Question: What model are the text features used in to provide predictions?
Paper: Multi-Head Decoder for End-to-End Speech Recognition
Automatic speech recognition (ASR) is the task to convert a continuous speech signal into a sequence of discrete characters, and it is a key technology to realize the interaction between human and machine. ASR has a great potential for various applications such as voice search and voice input, making our lives more rich. Typical ASR systems BIBREF0 consist of many modules such as an acoustic model, a lexicon model, and a language model. Factorizing the ASR system into these modules makes it possible to deal with each module as a separate problem. Over the past decades, this factorization has been the basis of the ASR system, however, it makes the system much more complex.
Question: By how much does their method outperform the multi-head attention model?
Answer: Their average improvement in Character Error Rate over the best MHA model was 0.33 percent points.
Paper: Multi-Head Decoder for End-to-End Speech Recognition
The overview of attention-based network architecture is shown in Fig. FIGREF1 .
Question: How large is the corpus they use?
Answer: 449050
Paper: Multi-Head Decoder for End-to-End Speech Recognition
The overview of our proposed multi-head decoder (MHD) architecture is shown in Fig. FIGREF19 . In MHD architecture, multiple attentions are calculated with the same manner in the conventional multi-head attention (MHA) BIBREF12 . We first describe the conventional MHA, and extend it to our proposed multi-head decoder (MHD).
Question: Does each attention head in the decoder calculate the same output?
Paper: Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
Hierarchical relationships play a central role in knowledge representation and reasoning. Hypernym detection, i.e., the modeling of word-level hierarchies, has long been an important task in natural language processing. Starting with BIBREF0 , pattern-based methods have been one of the most influential approaches to this problem. Their key idea is to exploit certain lexico-syntactic patterns to detect is-a relations in text. For instance, patterns like “ INLINEFORM0 such as INLINEFORM1 ”, or “ INLINEFORM2 and other INLINEFORM3 ” often indicate hypernymy relations of the form INLINEFORM4 is-a INLINEFORM5 . Such patterns may be predefined, or they may be learned automatically BIBREF1 , BIBREF2 . However, a well-known problem of Hearst-like patterns is their extreme sparsity: words must co-occur in exactly the right configuration, or else no relation can be detected.
Question: Which distributional methods did they consider?
Paper: Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
In the following, we discuss pattern-based and distributional methods to detect hypernymy relations. We explicitly consider only relatively simple pattern-based approaches that allow us to directly compare their performance to DIH-based methods.
Question: Which benchmark datasets are used?
Paper: Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
First, let INLINEFORM0 denote the set of hypernymy relations that have been extracted via Hearst patterns from a text corpus INLINEFORM1 . Furthermore let INLINEFORM2 denote the count of how often INLINEFORM3 has been extracted and let INLINEFORM4 denote the total number extractions. In the first, most direct application of Hearst patterns, we then simply use the counts INLINEFORM5 or, equivalently, the extraction probability DISPLAYFORM0 
Question: What hypernymy tasks do they study?