pwc-archive/papers-with-abstracts huggingface dataset:
HLTRI at W-NUT 2020 Shared Task-3: COVID-19 Event Extraction from Twitter Using Multi-Task Hopfield Pooling
Authors: Maxwell Weinzierl, Sanda Harabagiu
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (WNUT) 2020 11
Tasks: Event Extraction, Language Modeling, Language Modelling

Extracting structured knowledge involving self-reported events related to the COVID-19 pandemic from Twitter has the potential to inform surveillance systems that play a critical role in public health. The event extraction challenge presented by the W-NUT 2020 Shared Task 3 focused on the identification of five types of events relevant to the COVID-19 pandemic and their respective set of pre-defined slots encoding demographic, epidemiological, clinical as well as spatial, temporal or subjective knowledge. Our participation in the challenge led to the design of a neural architecture for jointly identifying all Event Slots expressed in a tweet relevant to an event of interest. This architecture uses COVID-Twitter-BERT as the pre-trained language model. In addition, to learn text span embeddings for each Event Slot, we relied on a special case of Hopfield Networks, namely Hopfield pooling. The results of the shared task evaluation indicate that our system performs best when it is trained on a larger dataset, while it remains competitive when training on smaller datasets.
Annotating Topics, Stance, Argumentativeness and Claims in Dutch Social Media Comments: A Pilot Study
Authors: Nina Bauwelinck, Els Lefever
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Articles

One of the major challenges currently facing the field of argumentation mining is the lack of consensus on how to analyse argumentative user-generated texts such as online comments. The theoretical motivations underlying the annotation guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. This pilot study reports on the annotation of a corpus of 100 Dutch user comments made in response to politically-themed news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement scores for argumentativeness detection (0.76 Fleiss’ kappa) and moderate agreement for claim labelling (0.45 Fleiss’ kappa). We provide a clear justification of the theories and definitions underlying the design of our guidelines. Our analysis of the annotations signal the importance of adjusting our guidelines to include allowances for missing context information and defining the concept of argumentativeness in connection with stance. Our annotated corpus and associated guidelines are made publicly available.
Exploring Morality in Argumentation
Authors: Jonathan Kobbe, Ines Rehbein, Ioana Hulpuș, Heiner Stuckenschmidt
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Argument Mining

Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated testset. We then apply our models to investigate how moral values in arguments relate to argument quality, stance and audience reactions.
Annotation and Detection of Arguments in Tweets
Authors: Robin Schaefer, Manfred Stede
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Argument Mining

Notwithstanding the increasing role Twitter plays in modern political and social discourse, resources built for conducting argument mining on tweets remain limited. In this paper, we present a new corpus of German tweets annotated for argument components. To the best of our knowledge, this is the first corpus containing not only annotated full tweets but also argumentative spans within tweets. We further report first promising results using supervised classification (F1: 0.82) and sequence labeling (F1: 0.72) approaches.
News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models
Authors: Mark Carlebach, Ria Cheruvu, Brandon Walker, Cesar Ilharco Magalhaes, Sylvain Jaume
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Articles, Clustering, Natural Language Inference, Semantic Similarity, Semantic Textual Similarity

Today’s news volume makes it impractical for readers to get a diverse and comprehensive view of published articles written from opposing viewpoints. We introduce a transformer-based news aggregation system, composed of topic modeling, semantic clustering, claim extraction, and textual entailment that identifies viewpoints presented in articles within a semantic cluster and classifies them into positive, neutral and negative entailments. Our novel embedded topic model using BERT-based embeddings outperforms baseline topic modeling algorithms by an 11% relative improvement. We compare recent semantic similarity models in the context of news aggregation, evaluate transformer-based models for claim extraction on news data, and demonstrate the use of textual entailment models for diverse viewpoint identification.
ECHR: Legal Corpus for Argument Mining
Authors: Prakash Poudyal, Jaromir Savelka, Aagje Ieven, Marie Francine Moens, Teresa Goncalves, Paulo Quaresma
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Argument Mining, Relation, Relation Prediction

In this paper, we publicly release an annotated corpus of 42 decisions of the European Court of Human Rights (ECHR). The corpus is annotated in terms of three types of clauses useful in argument mining: premise, conclusion, and non-argument parts of the text. Furthermore, relationships among the premises and conclusions are mapped. We present baselines for three tasks that lead from unstructured texts to structured arguments. The tasks are argument clause recognition, clause relation prediction, and premise/conclusion recognition. Despite a straightforward application of the bidirectional encoders from Transformers (BERT), we obtained very promising results F1 0.765 on argument recognition, 0.511 on relation prediction, and 0.859/0.628 on premise/conclusion recognition). The results suggest the usefulness of pre-trained language models based on deep neural network architectures in argument mining. Because of the simplicity of the baselines, there is ample space for improvement in future work based on the released corpus.
Argument from Old Man’s View: Assessing Social Bias in Argumentation
Authors: Maximilian Spliethöver, Henning Wachsmuth
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Word Embeddings

Social bias in language - towards genders, ethnicities, ages, and other social groups - poses a problem with ethical impact for many NLP applications. Recent research has shown that machine learning models trained on respective data may not only adopt, but even amplify the bias. So far, however, little attention has been paid to bias in computational argumentation. In this paper, we study the existence of social biases in large English debate portals. In particular, we train word embedding models on portal-specific corpora and systematically evaluate their bias using WEAT, an existing metric to measure bias in word embeddings. In a word co-occurrence analysis, we then investigate causes of bias. The results suggest that all tested debate corpora contain unbalanced and biased data, mostly in favor of male people with European-American names. Our empirical insights contribute towards an understanding of bias in argumentative data sources.
Use of Claim Graphing and Argumentation Schemes in Biomedical Literature: A Manual Approach to Analysis
Authors: Eli Moser, Robert E. Mercer
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Articles

Argumentation in an experimental life science paper consists of a main claim being supported with reasoned argumentative steps based on the data garnered from the experiments that were carried out. In this paper we report on an investigation of the large scale argumentation structure found when examining five biochemistry journal publications. One outcome of this investigation of biochemistry articles suggests that argumentation schemes originally designed for genetic research articles may transfer to experimental biomedical literature in general. Our use of these argumentation schemes shows that claims depend not only on experimental data but also on other claims. The tendency for claims to use other claims as their supporting evidence in addition to the experimental data led to two novel models that have provided a better understanding of the large scale argumentation structure of a complete biochemistry paper. First, the claim graph displays the claims within a paper, their interactions, and their evidence. Second, another aspect of this argumentation network is further illustrated by the Model of Informational Hierarchy (MIH) which visualizes at a meta-level the flow of reasoning provided by the authors of the paper and also connects the main claim to the paper’s title. Together, these models, which have been produced by a manual examination of the biochemistry articles, would be likely candidates for a computational method that analyzes the large scale argumentation structure.
Annotating argumentation in Swedish social media
Authors: Anna Lindahl
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12

This paper presents a small study of annotating argumentation in Swedish social media. Annotators were asked to annotate spans of argumentation in 9 threads from two discussion forums. At the post level, Cohen’s k and Krippendorff’s alpha 0.48 was achieved. When manually inspecting the annotations the annotators seemed to agree when conditions in the guidelines were explicitly met, but implicit argumentation and opinions, resulting in annotators having to interpret what’s missing in the text, caused disagreements.
Style Analysis of Argumentative Texts by Mining Rhetorical Devices
Authors: Khalid Al Khatib, Viorel Morari, Benno Stein
Date: 2020-12-01 00:00:00
Proceeding: COLING (ArgMining) 2020 12
Tasks: Articles

Using the appropriate style is key for writing a high-quality text. Reliable computational style analysis is hence essential for the automation of nearly all kinds of text synthesis tasks. Research on style analysis focuses on recognition problems such as authorship identification; the respective technology (e.g., n-gram distribution divergence quantification) showed to be effective for discrimination, but inappropriate for text synthesis since the “essence of a style” remains implicit. This paper contributes right here: it studies the automatic analysis of style at the knowledge-level based on rhetorical devices. To this end, we developed and evaluated a grammar-based approach for identifying 26 syntax-based devices. Then, we employed that approach to distinguish various patterns of style in selected sets of argumentative articles and presidential debates. The patterns reveal several insights into the style used there, while being adequate for integration in text synthesis systems.
Universal Recurrent Neural Network Grammar
Authors: Chinmay Choudhary, Colm O’riordan
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Constituency Parsing, Cross-Lingual Transfer, Transfer Learning

Modern approaches to Constituency Parsing are mono-lingual supervised approaches which require large amount of labelled data to be trained on, thus limiting their utility to only a handful of high-resource languages. To address this issue of data-sparsity for low-resource languages we propose Universal Recurrent Neural Network Grammars (UniRNNG) which is a multi-lingual variant of the popular Recurrent Neural Network Grammars (RNNG) model for constituency parsing. UniRNNG involves Cross-lingual Transfer Learning for Constituency Parsing task. The architecture of UniRNNG is inspired by Principle and Parameter theory proposed by Noam Chomsky. UniRNNG utilises the linguistic typology knowledge available as feature-values within WALS database, to generalize over multiple languages. Once trained on sufficiently diverse polyglot corpus UniRNNG can be applied to any natural language thus making it Language-agnostic constituency parser. Experiments reveal that our proposed UniRNNG outperform state-of-the-art baseline approaches for most of the target languages, for which these are tested.
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics
Authors: Jia-Yi Liao, Ya-Hsuan Lin, Kuan-Cheng Lin, Jia-Wei Chang
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Classification, Emotion Classification, Emotion Recognition, Information Retrieval, Music Emotion Recognition, Music Information Retrieval, Recommendation Systems, Retrieval, Transfer Learning

The explosive growth of music libraries has made music information retrieval and recommendation a critical issue. Recommendation systems based on music emotion recognition are gradually gaining attention. Most of the studies focus on audio data rather than lyrics to build models of music emotion classification. In addition, because of the richness of English language resources, most of the existing studies are focused on English lyrics but rarely on Chinese. For this reason, We propose an approach that uses the BERT pretraining model and Transfer learning to improve the emotion classification task of Chinese lyrics. The following approaches were used without any specific training for the Chinese lyrics emotional classification task: (a) Using BERT, only can reach 50% of the classification accuracy. (b) Using BERT with transfer learning of CVAW, CVAP, and CVAT datasets can achieve 71% classification accuracy.
Nested Named Entity Recognition for Chinese Electronic Health Records with QA-based Sequence Labeling
Authors: Yu-Lun Chiang, Chih-Hao Lin, Cheng-Lung Sung, Keh-Yih Su
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition

This study presents a novel QA-based sequence labeling (QASL) approach to naturally tackle both flat and nested Named Entity Recogntion (NER) tasks on a Chinese Electronic Health Records (CEHRs) dataset. This proposed QASL approach parallelly asks a corresponding natural language question for each specific named entity type, and then identifies those associated NEs of the same specified type with the BIO tagging scheme. The associated nested NEs are then formed by overlapping the results of various types. In comparison with those pure sequence-labeling (SL) approaches, since the given question includes significant prior knowledge about the specified entity type and the capability of extracting NEs with different types, the performance for nested NER task is thus improved, obtaining 90.70% of F1-score. Besides, in comparison with the pure QA-based approach, our proposed approach retains the SL features, which could extract multiple NEs with the same types without knowing the exact number of NEs in the same passage in advance. Eventually, experiments on our CEHR dataset demonstrate that QASL-based models greatly outperform the SL-based models by 6.12% to 7.14% of F1-score.
AI Clerk Platform : Information Extraction DIY Platform
Authors: Ru-Yng Chang, Wen-Lun Chen, Cheng-Ju Kao
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10

Information extraction is a core technology of natural language processing, which extracts some meaningful phrases/clauses from unstructured or semistructured content to a particular topic. It can be said to be the core technology of many language technologies and applications. This paper introduces AI Clerk Platform, which aims to accelerate and improve the entire process and convenience of the development of information extraction tools. AI Clerk Platform provides a friendly and intuitive visualized manual labeling interface, sets suitable semantic label in need, and implements, distributes and controls manual labeling tasks, so that users can complete customized information extraction models without programming and view the automatically predict results of models by three method. AI Clerk Platform further assists in the development of other natural language processing technologies and the derivation of application services.
A Flexible and Extensible Framework for Multiple Answer Modes Question Answering
Authors: Cheng-Chung Fan, Chia-Chih Kuo, Shang-Bao Luo, Pei-Jun Liao, Kuang-Yu Chang, Chiao-Wei Hsu, Meng-Tse Wu, Shih-Hong Tsai, Tzu-Man Wu, Aleksandra Smolka, Chao-Chun Liang, Hsin-Min Wang, Kuan-Yu Chen, Yu Tsao, Keh-Yih Su
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Answer Generation, Question Answering

This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4%.
Speech Emotion Recognition Based on CNN+LSTM Model
Authors: Wei Mou, Pei-Hsuan Shen, Chu-Yun Chu, Yu-Cheng Chiu, Tsung-Hsien Yang, Ming-Hsiang Su
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Emotion Recognition, model, Speech Emotion Recognition

Due to the popularity of intelligent dialogue assistant services, speech emotion recognition has become more and more important. In the communication between humans and machines, emotion recognition and emotion analysis can enhance the interaction between machines and humans. This study uses the CNN+LSTM model to implement speech emotion recognition (SER) processing and prediction. From the experimental results, it is known that using the CNN+LSTM model achieves better performance than using the traditional NN model.
A Study on Contextualized Language Modeling for Machine Reading Comprehension
Authors: Chin-Ying Wu, Yung-Chang Hsu, Berlin Chen
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Ensemble Learning, Language Modeling, Language Modelling, Machine Reading Comprehension, Reading Comprehension

With the recent breakthrough of deep learning technologies, research on machine reading comprehension (MRC) has attracted much attention and found its versatile applications in many use cases. MRC is an important natural language processing (NLP) task aiming to assess the ability of a machine to understand natural language expressions, which is typically operationalized by first asking questions based on a given text paragraph and then receiving machine-generated answers in accordance with the given context paragraph and questions. In this paper, we leverage two novel pretrained language models built on top of Bidirectional Encoder Representations from Transformers (BERT), namely BERT-wwm and MacBERT, to develop effective MRC methods. In addition, we also seek to investigate whether additional incorporation of the categorical information about a context paragraph can benefit MRC or not, which is achieved based on performing context paragraph clustering on the training dataset. On the other hand, an ensemble learning approach is proposed to harness the synergistic power of the aforementioned two BERT-based models so as to further promote MRC performance.
Discussion on the relationship between elders’ daily conversations and cognitive executive function: using word vectors and regression models
Authors: Ming-Hsiang Su, Yu-An Ko, Man-Ying Wang
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10

As the average life expectancy of Chinese people rises, the health care problems of the elderly are becoming more diverse, and the demand for long-term care is also increasing. Therefore, how to help the elderly have a good quality of life and maintain their dignity is what we need to think about. This research intends to explore the characteristics of natural language of normal aging people through a deep model. First, we collect information through focus groups so that the elders can naturally interact with other participants in the process. Then, through the word vector model and regression model, an executive function prediction model based on dialogue data is established to help understand the degradation trajectory of executive function and establish an early warning.
Chinese Medical Speech Recognition with Punctuated Hypothesis
Authors: Sheng-Luen Chung, Jin-Huan Fan, Hsien-Wei Ting
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic Speech Recognition (ASR) technology presents the possibility for medical professionals to document patient record, diagnosis, postoperative care, patrol records, and etc. that are now done manually. However, earlier research aimed on Chinese medical speech corpus (ChiMeS) has two shortcomings: first is the lack of punctuation, resulting in reduced readability of the output transcript, and second is the poor recognition error rate, affecting its application put to the fields. Accordingly, the contributions of this paper consist of: (1) A punctuated Chinese medical corpus psChiMeS-14 newly annotated from ChiMeS-14, which is the collection of 516 anonymized medical record readouts of 867 minutes long, recorded by 15 professional nursing staff from Taipei Hospital of the Ministry of Health and Welfare. psChiMeS-14 is manually punctuated with: colons, commas, and periods, ready for general end-to-end ASR models. (2) A self-attention based speech recognition solution by conformer networks. Trained by and tested on psChiMeS-14 corpus, the solutions deliver state-of-the-art recognition performance: CER (character error rate) 10.5%, and KER (Keyword error rate) of 13.10%, respectively, which is contrasted to the 15.70% CER and the 22.50% KER by an earlier reported Joint CTC/Attention architecture.