UNIST-Eunchan/NLP-Paper-to-QA-Generation huggingface dataset:
Abstract: Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this paper, we consider one aspect of embedding spaces, namely their stability. We show that even relatively high frequency words (100-200 occurrences) are often unstable. We provide empirical evidence for how various factors contribute to the stability of word embeddings, and we analyze the effects of stability on downstream tasks.

Introduction: Word embeddings are low-dimensional, dense vector representations that capture semantic properties of words. Recently, they have gained tremendous popularity in Natural Language Processing (NLP) and have been used in tasks as diverse as text similarity BIBREF0 , part-of-speech tagging BIBREF1 , sentiment analysis BIBREF2 , and machine translation BIBREF3 . Although word embeddings are widely used across NLP, their stability has not yet been fully evaluated and understood. In this paper, we explore the factors that play a role in the stability of word embeddings, including properties of the data, properties of the algorithm, and properties of the words. We find that word embeddings exhibit substantial instabilities, which can have implications for downstream tasks. Using the overlap between nearest neighbors in an embedding space as a measure of stability (see sec:definingStability below for more information), we observe that many common embedding spaces have large amounts of instability. For example, Figure FIGREF1 shows the instability of the embeddings obtained by training word2vec on the Penn Treebank (PTB) BIBREF4 . As expected, lower frequency words have lower stability and higher frequency words have higher stability. What is surprising however about this graph is the medium-frequency words, which show huge variance in stability. This cannot be explained by frequency, so there must be other factors contributing to their instability. In the following experiments, we explore which factors affect stability, as well as how this stability affects downstream tasks that word embeddings are commonly used for. To our knowledge, this is the first study comprehensively examining the factors behind instability.

Question: How is unstability defined?
Answer: An embedding is unstable if it has a low number of nearest neighbor embeddings of the words within the same frequency bucket.
Abstract: We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.

Introduction: Multi-task learning (MTL) is a recently resurgent approach to machine learning in which multiple tasks are simultaneously learned. By optimising the multiple loss functions of related tasks at once, multi-task learning models can achieve superior results compared to models trained on a single task. The key principle is summarized by BIBREF0 as “MTL improves generalization by leveraging the domain-specific information contained in the training signals of related tasks". Neural MTL has become an increasingly successful approach by exploiting similarities between Natural Language Processing (NLP) tasks BIBREF1 , BIBREF2 , BIBREF3 . Our work builds upon BIBREF4 , who demonstrate that employing semantic tagging as an auxiliary task for Universal Dependency BIBREF5 part-of-speech tagging can lead to improved performance. The objective of this paper is to investigate whether learning to predict lexical semantic categories can be beneficial to other NLP tasks. To achieve this we augment single-task models (ST) with an additional classifier to predict semantic tags and jointly optimize for both the original task and the auxiliary semantic tagging task. Our hypothesis is that learning to predict semantic tags as an auxiliary task can improve performance of single-task systems. We believe that this is, among other factors, due to the following: We test our hypothesis on three disparate NLP tasks: (i) Universal Dependency part-of-speech tagging (UPOS), (ii) Universal Dependency parsing (UD DEP), a complex syntactic task; and (iii) Natural Language Inference (NLI), a complex task requiring deep natural language understanding.

Question: What set of semantic tags did they use?
Answer: Tags categories ranging from anaphoric (definite, possessive pronoun), attribute (colour, concrete quantity, intersective, relation), unnamed entity (concept), logical (alternative, disjunction), discourse (subordinate relation), events (present simple, past simple), etc.
Abstract: Yor\`ub\'a is a widely spoken West African language with a writing system rich in orthographic and tonal diacritics. They provide morphological information, are crucial for lexical disambiguation, pronunciation and are vital for any computational Speech or Natural Language Processing tasks. However diacritic marks are commonly excluded from electronic texts due to limited device and application support as well as general education on proper usage. We report on recent efforts at dataset cultivation. By aggregating and improving disparate texts from the web and various personal libraries, we were able to significantly grow our clean Yor\`ub\'a dataset from a majority Bibilical text corpora with three sources to millions of tokens from over a dozen sources. We evaluate updated diacritic restoration models on a new, general purpose, public-domain Yor\`ub\'a evaluation dataset of modern journalistic news text, selected to be multi-purpose and reflecting contemporary usage. All pre-trained models, datasets and source-code have been released as an open-source project to advance efforts on Yor\`ub\'a language technology.

Introduction: Yorùbá is a tonal language spoken by more than 40 Million people in the countries of Nigeria, Benin and Togo in West Africa. The phonology is comprised of eighteen consonants, seven oral vowel and five nasal vowel phonemes with three kinds of tones realized on all vowels and syllabic nasal consonants BIBREF0. Yorùbá orthography makes notable use of tonal diacritics, known as amí ohùn, to designate tonal patterns, and orthographic diacritics like underdots for various language sounds BIBREF1, BIBREF2. Diacritics provide morphological information, are crucial for lexical disambiguation and pronunciation, and are vital for any computational Speech or Natural Language Processing (NLP) task. To build a robust ecosystem of Yorùbá-first language technologies, Yorùbá text must be correctly represented in computing environments. The ultimate objective of automatic diacritic restoration (ADR) systems is to facilitate text entry and text correction that encourages the correct orthography and promotes quotidian usage of the language in electronic media.

Question: What sources did they get the data from?
Answer: Various web resources and couple of private sources as listed in the table.
Abstract: The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360{\deg} Stance Detection, a tool that aggregates news with multiple perspectives on a topic. It presents them on a spectrum ranging from support to opposition, enabling the user to base their opinion on multiple pieces of diverse evidence.

Introduction: The growing epidemic of fake news in the wake of the election cycle for the 45th President of the United States has revealed the danger of staying within our filter bubbles. In light of this development, research in detecting false claims has received renewed interest BIBREF0 . However, identifying and flagging false claims may not be the best solution, as putting a strong image, such as a red flag, next to an article may actually entrench deeply held beliefs BIBREF1 . A better alternative would be to provide additional evidence that will allow a user to evaluate multiple viewpoints and decide with which they agree. To this end, we propose 360° INLINEFORM0 INLINEFORM1 Stance Detection, a tool that provides a wide view of a topic from different perspectives to aid with forming a balanced opinion. Given a topic, the tool aggregates relevant news articles from different sources and leverages recent advances in stance detection to lay them out on a spectrum ranging from support to opposition to the topic. Stance detection is the task of estimating whether the attitude expressed in a text towards a given topic is `in favour', `against', or `neutral'. We collected and annotated a novel dataset, which associates news articles with a stance towards a specified topic. We then trained a state-of-the-art stance detection model BIBREF2 on this dataset. The stance detection model is integrated into the 360° INLINEFORM0 INLINEFORM1 Stance Detection website as a web service. Given a news search query and a topic, the tool retrieves news articles matching the query and analyzes their stance towards the topic. The demo then visualizes the articles as a 2D scatter plot on a spectrum ranging from `against' to `in favour' weighted by the prominence of the news outlet and provides additional links and article excerpts as context. The interface allows the user to obtain an overview of the range of opinion that is exhibited towards a topic of interest by various news outlets. The user can quickly collect evidence by skimming articles that fall on different parts of this opinion spectrum using the provided excerpts or peruse any of the original articles by following the available links.

Question: what dataset did they use for this tool?
Answer: They collect data using the AYLIEN News API,  which provides search capabilities for news articles enriched with extracted entities and other metadata and take a step to compile a curated list of topics. The final dataset consists of 32,227 pairs of news articles and topics annotated with their stance.
Abstract: For the webportal"Who is in the News!"with statistics about the appearence of persons in written news we developed an extension, which measures the relationship of public persons depending on a time parameter, as the relationship may vary over time. On a training corpus of English and German news articles we built a measure by extracting the persons occurrence in the text via pretrained named entity extraction and then construct time series of counts for each person. Pearson correlation over a sliding window is then used to measure the relation of two persons.

Introduction: “Who is in the News!” is a webportal with statistics and plots about the appearence of persons in written news articles. It counts how often public persons are mentioned in news articles and can be used for research or journalistic purposes. The application is indexing articles published by “Reuters” agency on their website . With the interactive charts users can analyze different timespans for the mentiones of public people and look for patterns in the data. The portal is bulit with the Python microframework “Dash" which uses the plattform “Plotly" for the interactive charts. Playing around with the charts shows some interresting patterns like the one in the example of Figure FIGREF5 . This figure suggests that there must be some relationship between this two persons. In this example it is obvious because the persons are both german politicians and candidates for the elections. This motivated us to look for suitalbe measures to caputure how persons are related to each other, which then can be used to exted the webportal with charts showing the person to person relationships. Relationship and distance between persons have been analyzed for decades, for example BIBREF0 looked at distance in the famous experimental study “the Small World Problem”. They inspected the graph of relationships between different persons and set the “distance” to the shortest path between them. Other approaches used large text corpora for trying to find connections and relatedness by making statistics over the words in the texts. This of course only works for people appearing in the texts and we will discuss this in section SECREF2 . All these methods do not cover the changes of relations of the persons over time, that may change over the years. Therefore the measure should have a time parameter, which can be set to the desired time we are investigating. We have developed a method for such a measure and tested it on a set of news articles for the United States and Germany. In Figure FIGREF6 you see how the relation changes in an example of the German chancellor ”Angela Merkel” and her opponent on the last elections “Martin Schulz”. It starts around 0 in 2015 and goes up to about 0.75 in 2017 as we can expect looking at the high correlated time series chart in Figure FIGREF5 from the end of 2017.

Question: How large is the dataset?
Answer: 70287
Abstract: Social Media users tend to mention entities when reacting to news events. The main purpose of this work is to create entity-centric aggregations of tweets on a daily basis. By applying topic modeling and sentiment analysis, we create data visualization insights about current events and people reactions to those events from an entity-centric perspective.

Introduction: Entities play a central role in the interplay between social media and online news BIBREF0 . Everyday millions of tweets are generated about local and global news, including people reactions and opinions regarding the events displayed on those news stories. Trending personalities, organizations, companies or geographic locations are building blocks of news stories and their comments. We propose to extract entities from tweets and their associated context in order to understand what is being said on Twitter about those entities and consequently to create a picture of people reactions to recent events. With this in mind and using text mining techniques, this work explores and evaluates ways to characterize given entities by finding: (a) the main terms that define that entity and (b) the sentiment associated with it. To accomplish these goals we use topic modeling BIBREF1 to extract topics and relevant terms and phrases of daily entity-tweets aggregations, as well as, sentiment analysis BIBREF2 to extract polarity of frequent subjective terms associated with the entities. Since public opinion is, in most cases, not constant through time, this analysis is performed on a daily basis. Finally we create a data visualization of topics and sentiment that aims to display these two dimensions in an unified and intelligible way. The combination of Topic Modeling and Sentiment Analysis has been attempted before: one example is a model called TSM - Topic-Sentiment Mixture Model BIBREF3 that can be applied to any Weblog to determine a correlation between topic and sentiment. Another similar model has been proposed proposed BIBREF4 in which the topic extraction is achieved using LDA, similarly to the model that will be presented. Our work distinguishes from previous work by relying on daily entity-centric aggregations of tweets to create a meta-document which will be used as input for topic modeling and sentiment analysis.

Question: What model was used for sentiment analysis?
Answer: Lexicon based word-level  SA.
Abstract: The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at this https URL

Introduction: In today's day and age of social media, there are ample opportunities for fake news production, dissemination and consumption. BIBREF0 break down fake news into three categories, hoax, propaganda and satire. A hoax article typically tries to convince the reader about a cooked-up story while propaganda ones usually mislead the reader into believing a false political or social agenda. BIBREF1 defines a satirical article as the one which deliberately exposes real-world individuals, organisations and events to ridicule. Previous works BIBREF2, BIBREF0 rely on various linguistic and hand-crafted semantic features for differentiating between news articles. However, none of them try to model the interaction of sentences within the document. We observed a pattern in the way sentences cluster in different kind of news articles. Specifically, satirical articles had a more coherent story and thus all the sentences in the document seemed similar to each other. On the other hand, the trusted news articles were also coherent but the similarity between sentences from different parts of the document was not that strong, as depicted in Figure FIGREF1. We believe that the reason for such kind of behaviour is the presence of factual jumps across sections in a trusted document. In this work, we propose a graph neural network-based model to classify news articles while capturing the interaction of sentences across the document. We present a series of experiments on News Corpus with Varying Reliability dataset BIBREF0 and Satirical Legitimate News dataset BIBREF2. Our results demonstrate that the proposed model achieves state-of-the-art performance on these datasets and provides interesting insights. Experiments performed in out-of-domain settings establish the generalizability of our proposed method.

Question: What was their state of the art accuracy score?
Answer: In 2-way classification precision score was 88% and recall 82%. In 4-way classification on LUN-dev F1-score was 91% and on LUN-test F1-score was 65%.
Abstract: Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context -- both document and sentence level information -- than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.

Introduction: Named entity typing is the task of detecting the type (e.g., person, location, or organization) of a named entity in natural language text. Entity type information has shown to be useful in natural language tasks such as question answering BIBREF0 , knowledge-base population BIBREF1 , BIBREF2 , and co-reference resolution BIBREF3 . Motivated by its application to downstream tasks, recent work on entity typing has moved beyond standard coarse types towards finer-grained semantic types with richer ontologies BIBREF0 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . Rather than assuming an entity can be uniquely categorized into a single type, the task has been approached as a multi-label classification problem: e.g., in “... became a top seller ... Monopoly is played in 114 countries. ...” (fig:arch), “Monopoly” is considered both a game as well as a product. The state-of-the-art approach BIBREF8 for fine-grained entity typing employs an attentive neural architecture to learn representations of the entity mention as well as its context. These representations are then combined with hand-crafted features (e.g., lexical and syntactic features), and fed into a linear classifier with a fixed threshold. While this approach outperforms previous approaches which only use sparse binary features BIBREF4 , BIBREF6 or distributed representations BIBREF9 , it has a few drawbacks: (1) the representations of left and right contexts are learnt independently, ignoring their mutual connection; (2) the attention on context is computed solely upon the context, considering no alignment to the entity; (3) document-level contexts which could be useful in classification are not exploited; and (4) hand-crafted features heavily rely on system or human annotations. To overcome these drawbacks, we propose a neural architecture (fig:arch) which learns more context-aware representations by using a better attention mechanism and taking advantage of semantic discourse information available in both the document as well as sentence-level contexts. Further, we find that adaptive classification thresholds leads to further improvements. Experiments demonstrate that our approach, without any reliance on hand-crafted features, outperforms prior work on three benchmark datasets.

Question: What is the architecture of the model?
Answer: Document-level context encoder, entity and sentence-level context encoders with common attention, then logistic regression, followed by adaptive thresholds.