Argument mining tasks require an informed range of low to high complexity linguistic phenomena and commonsense knowledge. Previous work has shown that pre-trained language models are highly effective at encoding syntactic and semantic linguistic phenomena when applied with transfer learning techniques and built on different pre-training objectives. It remains an issue of how much the existing pre-trained language models encompass the complexity of argument mining tasks. We rely on experimentation to shed light on how language models obtained from different lexical semantic families leverage the performance of the identification of argumentative discourse units task. Experimental results show that transfer learning techniques are beneficial to the task and that current methods may be insufficient to leverage commonsense knowledge from different lexical semantic families.
Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health
Institute for Artificial Intelligence（人工智能研究院）；School of Psychological and Cognitive Sciences（心理学与认知科学学院）；School of Intelligence Science and Technology（智能科学与技术学院）；State Key Laboratory of General AI（通用人工智能国家重点实验室）；Beijing Key Laboratory of Behavior and Mental Health（北京行为与心理健康重点实验室）；PKU-Changsha Institute for Computing and Digital Economy（北京大学长沙计算与数字经济研究院）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；University of Potsdam（波茨坦大学）
Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-tex…
On Theoretically-Driven LLM Agents for Multi-Dimensional Discourse Analysis
Laboratory of The New Ethos, Warsaw University of Technology, Poland（新伦理实验室，华沙理工大学，波兰）；Faculty of Electronics and Information Technology, Warsaw University of Technology, Poland（电子与信息技术学院，华沙理工大学，波兰）
Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method
Lab of Artificial Intelligence for Education, East China Normal University（教育人工智能实验室，华东师范大学）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（教育人工智能上海研究院，华东师范大学）；School of Computer Science and Technology, East China Normal University（计算机科学与技术学院，华东师范大学）；Department of Chinese Language and Literature, East China Normal University（中文语言文学系，华东师范大学）；College of Education, Zhejiang University（教育学院，浙江大学）
Columbia University（哥伦比亚大学）；Data Science Institute（数据科学研究所）；Rutgers University（罗格斯大学）；School of Communication and Information（传播与信息学院）
In discourse studies concessions are considered among those argumentative strategies that increase persuasion. We aim to empirically test this hypothesis by calculating the distribution of argumentative concessions in persuasive vs. non-persuasive comments from the ChangeMyView subreddit. This constitutes a challenging task since concessions are not always part of an argument. Drawing from a theoretically-informed typology of concessions, we conduct an annotation task to label a set of polysemous lexical markers as introducing an argumentative concession or not and we observe their distributio…
When assessing relations between argumentative units (e.g., support or attack), computational systems often exploit disclosing indicators or markers that are not part of elementary argumentative units (EAUs) themselves, but are gained from their context (position in paragraph, preceding tokens, etc.). We show that this dependency is much stronger than previously assumed. In fact, we show that by completely masking the EAU text spans and only feeding information from their context, a competitive system may function even better. We argue that an argument analysis system that relies more on disco…
This paper is aimed at reporting on the development and application of a computer model for discourse analysis through segmentation. Segmentation refers to the principled division of texts into contiguous constituents. Other studies have looked at the application of a number of models to the analysis of discourse by computer. The segmentation procedure developed for the present investigation is called LSM ('Link Set Median'). It was applied to three corpus of 300 texts from three different genres. The results obtained by application of the LSM procedure on the corpus were then compared to segm…
The Pragmatic Persona: Discovering LLM Persona through Bridging Inference
Department of Artificial Intelligence, Chung-Ang University, Seoul, 06974, Republic of Korea（Chung-Ang大学人工智能系）；Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada（不列颠哥伦比亚大学计算机科学系）；Van Lang University, Ho Chi Minh City, Vietnam（文-lang大学）
Carnegie Mellon University（卡内基梅隆大学）；University of Dundee（邓迪大学）
While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations. Most recent studies rely on black-box models, which are not as linguistically insightful as desired. On the other hand, earlier studies use rather simple lexical features, missing logical relations between statements. To overcome these limitations, our work classifies argumentative relations based on four logical and theory-informed mechanisms between …
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Warwick（华威大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conve…
We present a computational exploration of argument critique writing by young students. Middle school students were asked to criticize an argument presented in the prompt, focusing on identifying and explaining the reasoning flaws. This task resembles an established college-level argument critique task. Lexical and discourse features that utilize detailed domain knowledge to identify critiques exist for the college task but do not perform well on the young students data. Instead, transformer-based architecture (e.g., BERT) fine-tuned on a large corpus of critique essays from the college task pe…
University of Copenhagen（哥本哈根大学）；CNRS（法国国家科学研究中心）；Univ Paris Diderot（巴黎狄德罗大学）
Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of t…
PERSPECTRA: A Scalable and Configurable Pluralist Benchmark of Perspectives from Arguments
Bonn-Aachen International Center for Information Technology, University of Bonn（波恩大学波恩-亚琛国际信息技术中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；University of Sheffield（谢菲尔德大学）；McMaster University（麦克马斯特大学）
Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究所）
Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data. In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and (2) adopting segment-lev…
Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)
Information and Processing Telecommunications Center（信息与处理电信中心）；Universidad Politécnica de Madrid（马德里理工大学）；Politecnico di Milano（米兰理工大学）；Banco de España（西班牙银行）
Centro de Investigación en Ciencias Cognitivas（认知科学研究中心）；Universidad Autónoma del Estado de Morelos（莫雷洛斯州立自治大学）；Centro de Investigación en Ciencias（科学研究中心）
The idea that discourse relations are construed through explicit content and shared, or implicit, knowledge between producer and interpreter is ubiquitous in discourse research and linguistics. However, the actual contribution of the lexical semantics of arguments is unclear. We propose a computational approach to the analysis of contrast and concession relations in the PDTB corpus. Our work sheds light on the extent to which lexical semantics contributes to signaling explicit and implicit discourse relations and clarifies the contribution of different parts of speech in both. This study contr…
Research in the social sciences and psychology has shown that the persuasiveness of an argument depends not only the language employed, but also on attributes of the source/communicator, the audience, and the appropriateness and strength of the argument's claims given the pragmatic and discourse context of the argument. Among these characteristics of persuasive arguments, prior work in NLP does not explicitly investigate the effect of the pragmatic and discourse context when determining argument quality. This paper presents a new dataset to initiate the study of this aspect of argumentation: i…
The automatic identification of discourse relations is still a challenging task in natural language processing. Discourse connectives, such as "since" or "but", are the most informative cues to identify explicit relations; however discourse parsers typically use a closed inventory of such connectives. As a result, discourse relations signaled by markers outside these inventories (i.e. AltLexes) are not detected as effectively. In this paper, we propose a novel method to leverage parallel corpora in text simplification and lexical resources to automatically identify alternative lexicalizations …
Calleva Research Centre, University of Oxford, UK（牛津大学卡勒瓦研究中心）；St John’s College, University of Cambridge, UK（剑桥大学圣约翰学院）；Montréal Neurological Institute, McGill University, Canada（蒙特利尔神经科学研究所，麦吉尔大学，加拿大）；Centre for Eudaimonia and Human Flourishing, University of Oxford, UK（幸福与人类繁荣中心，牛津大学，英国）；Department of Computing, Imperial College London, UK（伦敦帝国理工学院计算机系）
When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prio…
Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?
Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC)（人工智能与计算机科学实验室（LIACC））；Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院）；Natural Language Learning Group (NLLG)（自然语言学习组（NLLG））；Faculty of Technology, Bielefeld University（比勒费尔德大学技术学院）
LLMs for automatic annotation of Mandarin narrative transcripts
Shanghai Jiao Tong University（上海交通大学）；University of California San Diego（加州大学圣地亚哥分校）；The Hong Kong Polytechnic University（香港理工大学）；National Research Center for Language and Well-Being（语言与幸福感国家研究中心）；City University of Hong Kong（香港城市大学）
Vyatka State University（维亚特卡国立大学）；ITMO University（ITMO大学）
Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentative structure. A significant obstacle to research in this area for the Russian language is the lack of annotated Russian-language text corpora. This article explores the possibility of improving the quality of argumentation mining using the extension of the Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based on the machine translation of the Persuasive Essays Corpus (PersEssay…
Learning Research and Development Center（学习研究与发展中心）；University of Pittsburgh（匹兹堡大学）
Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models. In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. W…
This paper investigates the influence of discourse features on text complexity assessment. To do so, we created two data sets based on the Penn Discourse Treebank and the Simple English Wikipedia corpora and compared the influence of coherence, cohesion, surface, lexical and syntactic features to assess text complexity. Results show that with both data sets coherence features are more correlated to text complexity than the other types of features. In addition, feature selection revealed that with both data sets the top most discriminating feature is a coherence feature.
MOE Key Laboratory of Computational Linguistics, Peking University（北京大学教育部计算语言学重点实验室）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
In recent years, more research has been devoted to studying the subtask of the complete shallow discourse parsing, such as indentifying discourse connective and arguments of connective. There is a need to design a full discourse parser to pull these subtasks together. So we develop a discourse parser turning the free text into discourse relations. The parser includes connective identifier, arguments identifier, sense classifier and non-explicit identifier, which connects with each other in pipeline. Each component applies the maximum entropy model with abundant lexical and syntax features extr…
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Future Brain Education Technology Co., Ltd.（北京未来脑教育科技有限公司）；The Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；Guangxi Zhuang Autonomous Region Big Data Research Institute（广西壮族自治区大数据研究院）
We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive non-classical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logic…
