UNIST-Eunchan/NLP-Paper-to-QA-Generation huggingface dataset:
Abstract: Recent studies on knowledge base completion, the task of recovering missing facts based on observed facts, demonstrate the importance of learning embeddings from multi-step relations. Due to the size of knowledge bases, previous works manually design relation paths of observed triplets in symbolic space (e.g. random walk) to learn multi-step relations during training. However, these approaches suffer some limitations as most paths are not informative, and it is prohibitively expensive to consider all possible paths. To address the limitations, we propose learning to traverse in vector space directly without the need of symbolic space guidance. To remember the connections between related observed triplets and be able to adaptively change relation paths in vector space, we propose Implicit ReasoNets (IRNs), that is composed of a global memory and a controller module to learn multi-step relation paths in vector space and infer missing facts jointly without any human-designed procedure. Without using any axillary information, our proposed model achieves state-of-the-art results on popular knowledge base completion benchmarks.


Question: What is the source of the dataset?
Answer: Online sites tagged as fake news site by Verafiles and NUJP and news website in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera
Abstract: We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-depth analysis of the dataset, highlighting characteristics and challenges. Further, we present results for automatic veracity prediction, both with established baselines and with a novel method for joint ranking of evidence pages and predicting veracity that outperforms all baselines. Significant performance increases are achieved by encoding evidence, and by modelling metadata. Our best-performing model achieves a Macro F1 of 49.2%, showing that this is a challenging testbed for claim veracity prediction.

Introduction: 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: How much important is the visual grounding in the learning of the multilingual representations?
Answer: performance is significantly degraded without pixel data
Abstract: It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of graph embeddings. We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for measuring such bias. We conduct experiments on Wikidata and Freebase, and show that, as with word embeddings, harmful social biases related to professions are encoded in the embeddings with respect to gender, religion, ethnicity and nationality. For example, graph embeddings encode the information that men are more likely to be bankers, and women more likely to be homekeepers. As graph embeddings become increasingly utilized, we suggest that it is important the existence of such biases are understood and steps taken to mitigate their impact.

Introduction: Recent work in the word embeddings literature has shown that embeddings encode gender and racial biases, BIBREF0, BIBREF1, BIBREF2. These biases can have harmful effects in downstream tasks including coreference resolution, BIBREF3 and machine translation, BIBREF4, leading to the development of a range of methods to try to mitigate such biases, BIBREF0, BIBREF5. In an adjacent literature, learning embeddings of knowledge graph (KG) entities and relations is becoming an increasingly common first step in utilizing KGs for a range of tasks, from missing link prediction, BIBREF6, BIBREF7, to more recent methods integrating learned embeddings into language models, BIBREF8, BIBREF9, BIBREF10. A natural question to ask is “do graph embeddings encode social biases in similar fashion to word embeddings". We show that existing methods for identifying bias in word embeddings are not suitable for KG embeddings, and present an approach to overcome this using embedding finetuning. We demonstrate (perhaps unsurprisingly) that unequal distributions of people of different genders, ethnicities, religions and nationalities in Freebase and Wikidata result in biases related to professions being encoded in graph embeddings, such as that men are more likely to be bankers and women more likely to be homekeepers. Such biases are potentially harmful when KG embeddings are used in applications. For example, if embeddings are used in a fact checking task, they would make it less likely that we accept facts that a female entity is a politician as opposed to a male entity. Alternatively, as KG embeddings get utilized as input to language models BIBREF8, BIBREF9, BIBREF10, such biases can affect all downstream NLP tasks.

Question: How many layers of self-attention does the model have?
Answer: 1, 4, 8, 16, 32, 64
Abstract: De-identification is the process of removing 18 protected health information (PHI) from clinical notes in order for the text to be considered not individually identifiable. Recent advances in natural language processing (NLP) has allowed for the use of deep learning techniques for the task of de-identification. In this paper, we present a deep learning architecture that builds on the latest NLP advances by incorporating deep contextualized word embeddings and variational drop out Bi-LSTMs. We test this architecture on two gold standard datasets and show that the architecture achieves state-of-the-art performance on both data sets while also converging faster than other systems without the use of dictionaries or other knowledge sources.

Introduction: [block] 1 5mm * 2pt*22pt [block] 2.1 5mm [block] 2.1.1 5mm

Question: Which four languages do they experiment with?
Answer: German, English, Italian, Chinese
Abstract: The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting. These systems do not perform as well when applied in a cross-corpus and cross-language scenario. This paper presents results for speech emotion recognition for 4 languages in both single corpus and cross corpus setting. Additionally, since multi-task learning (MTL) with gender, naturalness and arousal as auxiliary tasks has shown to enhance the generalisation capabilities of the emotion models, this paper introduces language ID as another auxiliary task in MTL framework to explore the role of spoken language on emotion recognition which has not been studied yet.

Introduction: Speech conveys human emotions most naturally. In recent years there has been an increased research interest in speech emotion recognition domain. The first step in a typical SER system is extracting linguistic and acoustic features from speech signal. Some para-linguistic studies find Low-Level Descriptor (LLD) features of the speech signal to be most relevant to studying emotions in speech. These features include frequency related parameters like pitch and jitter, energy parameters like shimmer and loudness, spectral parameters like alpha ratio and other parameters that convey cepstral and dynamic information. Feature extraction is followed with a classification task to predict the emotions of the speaker. Data scarcity or lack of free speech corpus is a problem for research in speech domain in general. This also means that there are even fewer resources for studying emotion in speech. For those that are available are dissimilar in terms of the spoken language, type of emotion (i.e. naturalistic, elicited, or acted) and labelling scheme (i.e. dimensional or categorical). Across various studies involving SER we observe that performance of model depends heavily on whether training and testing is performed from the same corpus or not. Performance is best when focus is on a single corpus at a time, without considering the performance of model in cross-language and cross-corpus scenarios. In this work, we work with diverse SER datasets i.e. tackle the problem in both cross-language and cross-corpus setting. We use transfer learning across SER datasets and investigate the effects of language spoken on the accuracy of the emotion recognition system using our Multi-Task Learning framework. The paper is organized as follows: Section 2 reviewed related work on SER, cross-lingual and cross-corpus SER and the recent studies on role of language identification in speech emotion recognition system, Section 3 describes the datasets that have been used, Section 4 presents detailed descriptions of three types of SER experiments we conduct in this paper. In Section 5, we present our results and evaluations of our models. Section 6 presents some additional experiments to draw a direct comparison with previously published research. Finally, we discuss future work and conclude the paper.

Question: Does DCA or GMM-based attention perform better in experiments?
Answer: About the same performance
Abstract: In this paper, we demonstrate the importance of coreference resolution for natural language processing on the example of the TAC Slot Filling shared task. We illustrate the strengths and weaknesses of automatic coreference resolution systems and provide experimental results to show that they improve performance in the slot filling end-to-end setting. Finally, we publish KBPchains, a resource containing automatically extracted coreference chains from the TAC source corpus in order to support other researchers working on this topic.

Introduction: Coreference resolution systems group noun phrases (mentions) that refer to the same entity into the same chain. Mentions can be full names (e.g., John Miller), pronouns (e.g., he), demonstratives (e.g., this), comparatives (e.g., the first) or descriptions of the entity (e.g. the 40-year-old) BIBREF0 . Although coreference resolution has been a research focus for several years, systems are still far away from being perfect. Nevertheless, there are many tasks in natural language processing (NLP) which would benefit from coreference information, such as information extraction, question answering or summarization BIBREF1 . In BIBREF2 , for example, we showed that coreference information can also be incorporated into word embedding training. In general, coreference resolution systems can be used as a pre-processing step or as a part of a pipeline of different modules. Slot Filling is an information extraction task which has become popular in the last years BIBREF3 . It is a shared task organized by the Text Analysis Conference (TAC). The task aims at extracting information about persons, organizations or geo-political entities from a large collection of news, web and discussion forum documents. An example is “Steve Jobs” for the slot “X founded Apple”. Thinking of a text passage like “Steve Jobs was an American businessman. In 1976, he co-founded Apple”, it is clear that coreference resolution can play an important role for finding the correct slot filler value. In this study, we investigate how coreference resolution could help to improve performance on slot filling and which challenges exist. Furthermore, we present how we pre-processed the TAC source corpus with a coreference resolution system in order to be able to run the slot filling system more efficiently. In addition to this paper, we also publish the results of this pre-processing since it required long computation time and much resources.

Question: How are the potentially relevant text fragments identified?
Answer:  Generate a query out of the claim and querying a search engine, rank the words by means of TF-IDF, use IBM's AlchemyAPI to identify named entities, generate queries of 5–10 tokens, which execute against a search engine, and collect the snippets and the URLs in the results, skipping any result that points to a domain that is considered unreliable.
Abstract: Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.

Introduction:  $\dagger $ Corresponding author. This paper was partially supported by National Key Research and Development Program of China (No. 2017YFB0304100), National Natural Science Foundation of China (No. 61672343 and No. 61733011), Key Project of National Society Science Foundation of China (No. 15-ZDA041), The Art and Science Interdisciplinary Funds of Shanghai Jiao Tong University (No. 14JCRZ04). This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/. Recently, dialogue and interactive systems have been emerging with huge commercial values BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , especially in the e-commerce field BIBREF6 , BIBREF7 . Building a chatbot mainly faces two challenges, the lack of dialogue data and poor performance for multi-turn conversations. This paper describes a fine-grained information retrieval (IR) augmented multi-turn chatbot - Lingke. It can learn knowledge without human supervision from conversation records or given product introduction documents and generate proper response, which alleviates the problem of lacking dialogue corpus to train a chatbot. First, by using Apache Lucene to select top 2 sentences most relevant to the question and extracting subject-verb-object (SVO) triples from them, a set of candidate responses is generated. With regard to multi-turn conversations, we adopt a dialogue manager, including self-attention strategy to distill significant signal of utterances, and sequential utterance-response matching to connect responses with conversation utterances, which outperforms all other models in multi-turn response selection. An online demo is available via accessing http://47.96.2.5:8080/ServiceBot/demo/.

Question: How do they incorporate human advice?
Answer: by converting human advice to first-order logic format and use as an input to calculate gradient
Abstract: We consider the task of KBP slot filling -- extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relation extraction. Additionally, we demonstrate how several components such as weak supervision, word2vec features, joint learning and the use of human advice, can be incorporated in this relational framework. We evaluate the different components in the benchmark KBP 2015 task and show that RDNs effectively model a diverse set of features and perform competitively with current state-of-the-art relation extraction.

Introduction: The problem of knowledge base population (KBP) – constructing a knowledge base (KB) of facts gleaned from a large corpus of unstructured data – poses several challenges for the NLP community. Commonly, this relation extraction task is decomposed into two subtasks – entity linking, in which entities are linked to already identified identities within the document or to entities in the existing KB, and slot filling, which identifies certain attributes about a target entity. We present our work-in-progress for KBP slot filling based on our probabilistic logic formalisms and present the different components of the system. Specifically, we employ Relational Dependency Networks BIBREF0 , a formalism that has been successfully used for joint learning and inference from stochastic, noisy, relational data. We consider our RDN system against the current state-of-the-art for KBP to demonstrate the effectiveness of our probabilistic relational framework. Additionally, we show how RDNs can effectively incorporate many popular approaches in relation extraction such as joint learning, weak supervision, word2vec features, and human advice, among others. We provide a comprehensive comparison of settings such as joint learning vs learning of individual relations, use of weak supervision vs gold standard labels, using expert advice vs only learning from data, etc. These questions are extremely interesting from a general machine learning perspective, but also critical to the NLP community. As we show empirically, some of the results such as human advice being useful in many relations and joint learning being beneficial in the cases where the relations are correlated among themselves are on the expected lines. However, some surprising observations include the fact that weak supervision is not as useful as expected and word2vec features are not as predictive as the other domain-specific features. We first present the proposed pipeline with all the different components of the learning system. Next we present the set of 14 relations that we learn on before presenting the experimental results. We finally discuss the results of these comparisons before concluding by presenting directions for future research.

Question: What affective-based features are used?
Answer: affective features provided by different emotion models such as Emolex, EmoSenticNet, Dictionary of Affect in Language, Affective Norms for English Words and Linguistics Inquiry and Word Count
Abstract: We present ALL-IN-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.

Introduction: Customer feedback analysis is the task of classifying short text messages into a set of predefined labels (e.g., bug, request). It is an important step towards effective customer support. However, a real bottleneck for successful classification of customer feedback in a multilingual environment is the limited transferability of such models, i.e., typically each time a new language is encountered a new model is built from scratch. This is clearly impractical, as maintaining separate models is cumbersome, besides the fact that existing annotations are simply not leveraged. In this paper we present our submission to the IJCNLP 2017 shared task on customer feedback analysis, in which data from four languages was available (English, French, Japanese and Spanish). Our goal was to build a single system for all four languages, and compare it to the traditional approach of creating separate systems for each language. We hypothesize that a single system is beneficial, as it can provide positive transfer, particularly for the languages for which less data is available. The contributions of this paper are:

Question: What are the citation intent labels in the datasets?
Answer: Background, extends, uses, motivation, compare/contrast, and future work for the ACL-ARC dataset. Background, method, result comparison for the SciCite dataset.
Abstract: Africa has over 2000 languages. Despite this, African languages account for a small portion of available resources and publications in Natural Language Processing (NLP). This is due to multiple factors, including: a lack of focus from government and funding, discoverability, a lack of community, sheer language complexity, difficulty in reproducing papers and no benchmarks to compare techniques. To begin to address the identified problems, MASAKHANE, an open-source, continent-wide, distributed, online research effort for machine translation for African languages, was founded. In this paper, we discuss our methodology for building the community and spurring research from the African continent, as well as outline the success of the community in terms of addressing the identified problems affecting African NLP.

Introduction: 2144 of all 7111 (30.15%) living languages today are African languages BIBREF1. But only a small portion of linguistic resources for NLP research are built for African languages. As a result, there are only few NLP publications: In all ACL conferences in 2019, only 5 out of 2695 (0.19%) author affiliations were based in Africa BIBREF2. This stark contrast of linguistic richness versus poor representation of African languages in NLP is caused by multiple factors. First of all, African societies do not see hope for African languages being accepted as primary means of communication BIBREF3. As a result, few efforts to fund NLP or translation for African languages exist, despite the potential impact. This lack of focus has had a ripple effect. The few existing resources are not easily discoverable, published in closed journals, non-indexed local conferences, or remain undigitized, surviving only in private collections BIBREF4. This opaqueness impedes researchers' ability to reproduce and build upon existing results, and to develop, compete on and progress public benchmarks BIBREF5. African researchers are disproportionately affected by socio-economic factors, and are often hindered by visa issues BIBREF6 and costs of flights from and within Africa BIBREF7. They are distributed and disconnected on the continent, and rarely have the opportunity to commune, collaborate and share. Furthermore, African languages are of high linguistic complexity and variety, with diverse morphologies and phonologies, including lexical and grammatical tonal patterns, and many are practiced within multilingual societies with frequent code switching BIBREF8, BIBREF9, BIBREF10. Because of this complexity, cross-lingual generalization from success in languages like English are not guaranteed.

Question:  What percentage of improvement in inference speed is obtained by the proposed method over the newest state-of-the-art methods?
Answer: Across 4 datasets, the best performing proposed model (CNN) achieved an average of 363% improvement over the state of the art method (LR-CNN)