ARGUMENT MINING

Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset (N18-2)

Challenge:	Existing studies identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the holistic argument-level interactions.
Approach:	They propose to use machine reading comprehension to extract argument pairs from two documents . they propose to employ an AM query to identify all arguments in two documents, then an APE query to extract its paired arguments from another document.
Outcome:	The proposed method outperforms the state-of-the-art method by 7.11% in F1 score.

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Pavithra Rajendran, Danushka Bollegala, Simon Parsons

Text Classification

Challenge:	Argument mining is a subset of NLP that deals with extracting arguments from user-based content.
Approach:	They propose to use weakly supervised and semi-supervised methods to automatically annotate reviews and provide large annotated datasets.
Outcome:	The proposed methods can be used to learn better models for implicit/explicit opinion classification.

LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain (2025.findings-naacl)

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Joel Niklaus, Lucia Zheng, Arya D. McCarthy, Christopher Hahn, Brian M Rosen, Peter Henderson, Daniel E. Ho, Garrett Honke, Percy Liang, Christopher D Manning

Katarzyna Budzynska, Chris Reed

Challenge:	In general, instruction tuning is important for direct user interaction, but the legal domain is underrepresented in typical instruction datasets.
Approach:	They aggregate 58 annotated legal datasets and write instructions for each to create LawInstruct.
Outcome:	The proposed model improves on LegalBench across all model sizes, but no drop in MMLU.

Advances in Argument Mining (P19-4)

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Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)

Challenge:	Argument mining is a rapidly growing area of research and research that has seen significant growth over the past few years.
Approach:	Argument mining is a new area of research that uses opinion mining to extract opinions . the 6th ACL workshop on argument mining will be in Florence in 2019 .
Outcome:	Argument mining is a new area of research and development that has seen significant growth in the past three years.

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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic

Text Generation

Challenge:	Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments.
Approach:	They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate.
Outcome:	The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations.

Thesis Proposal: LLMs post-training for multilingual medical tasks. Instruction-Tuning, Continual-Pretraining or Reasoning? (2026.acl-srw)

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Pietro Ferrazzi, Alberto Lavelli, Bernardo Magnini

FEAT-writing: An Interactive Training System for Argumentative Writing (2025.coling-demos)

Challenge:	Adapting Large Language Models to the medical domain remains an active area of research .
Approach:	They propose to compare three common adaptation approaches to adapt large language models to the medical domain.
Outcome:	The proposed models are built on top of foundational LLMs and rely on different post-training methodologies for domain and task performance.

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Yuning Ding, Franziska Wehrhahn, Andrea Horbach

Multilingual Argument Mining: Datasets and Analysis (2020.findings-emnlp)

Challenge:	Argumentative writing is a critical skill for academic success, but many students struggle to develop these skills.
Approach:	They developed an online system that provides students with automated feedback and exercises for argumentative writing.
Outcome:	The proposed system improves argumentative writing quality among native English speakers and english-as-a-foreign-language learners.

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Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu, Artem Spector, Noam Slonim

Multilinguality

Challenge:	Argument mining tasks in non-English languages are dominated by English . we use a pre-trained language model that supports 104 languages to train models .
Approach:	They propose a multilingual BERT model to address argument mining tasks in non-English languages . they use English datasets and machine translation to facilitate transfer learning .
Outcome:	The proposed model is well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments.

Project Debater APIs: Decomposing the AI Grand Challenge (2021.emnlp-demo)

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Roy Bar-Haim, Yoav Kantor, Elad Venezian, Yoav Katz, Noam Slonim

Challenge:	Project Debater is the first AI system that can debate human experts on complex topics.
Approach:	They describe Project Debater's architecture and evaluate its performance . they will focus on Key Point Analysis, a novel technology that identifies main points .
Outcome:	The proposed system can debate human experts on complex topics.

Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes (2021.tacl-1)

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Yohan Jo, Seojin Bang, Chris Reed, Eduard Hovy

Text Classification

Challenge:	Recent studies have focused on training complex neural networks on labeled data.
Approach:	They propose to use logical mechanisms to classify argumentative relations without training on labeled data.
Outcome:	The proposed method classifies argumentative relations without training on labeled data significantly better than unsupervised baselines.

End-to-End Argument Mining as Biaffine Dependency Parsing (2021.eacl-main)

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Yuxiao Ye, Simone Teufel

Syntactic Text Processing

Challenge:	Argumentation mining (AM) is a new field of research that uses dependency parsing to analyse arguments.
Approach:	They propose a neural end-to-end approach to argument mining based on dependency parsing . their model is biaffine dependency parsed and outperforms the current state-of-the-art .
Outcome:	The proposed model outperforms the state-of-the-art in component identification and relation identification.

Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues (2022.coling-1)

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Sougata Saha, Souvik Das, Rohini K. Srihari

Multimodality

Challenge:	a recent work on argument mining has focused on parsing monologues, while neglecting dialogues.
Approach:	They propose an end-to-end argument parser that constructs argument graphs from dialogues . they use extensive pre-training and curriculum learning to train AM .
Outcome:	The proposed system performs all sub-tasks of AM and achieves significant improvements . it is compared to existing systems and validated through human evaluation .

A Bayesian Approach for Sequence Tagging with Crowds (D19-1)

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Edwin Simpson, Iryna Gurevych

Tagging

Challenge:	Existing methods for sequence tagging are data hungry and annotators are unreliable . current methods do not account for common types of span annotation error .
Approach:	They propose a Bayesian method for aggregating sequence tags that models sequential dependencies between annotations and ground-truth labels.
Outcome:	The proposed method outperforms existing methods on crowdsourced data and reduces crowdsourcing costs through active learning.

Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)

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Guizhen Chen, Liying Cheng, Anh Tuan Luu, Lidong Bing

The Discussion Tracker Corpus of Collaborative Argumentation (2020.lrec-1)

Challenge:	Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence.
Approach:	They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets .
Outcome:	The proposed model performs well on argument mining and argument generation tasks.

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Christopher Olshefski, Luca Lugini, Ravneet Singh, Diane Litman, Amanda Godley

A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality (2024.naacl-long)

Challenge:	The Discussion Tracker corpus is an annotated dataset of transcripts of spoken, multi-party argumentation transcribed from 985 minutes of audio .
Approach:	They analyze 29 multi-party arguments transcribed from 985 minutes of audio . they provide descriptive statistics and code for predicting each dimension separately.
Outcome:	The Discussion Tracker corpus was collected in high school English classes and annotated for argument moves, specificity, specificities and collaboration dimensions.

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Maja Stahl, Nadine Michel, Sebastian Kilsbach, Julian Schmidtke, Sara Rezat, Henning Wachsmuth

Looking at the Unseen: Effective Sampling of Non-Related Propositions for Argument Mining (2025.coling-main)

Challenge:	Existing arguments mining corpus with ground-truth quality annotations is lacking . authors propose baseline approaches to argument mining and essay scoring .
Approach:	They propose to use argumentative structure to support argumentative writing . they use an annotated german corpus to analyze interactions between the two tasks .
Outcome:	The proposed methods can be used to support argumentative writing . they analyze interactions between argumentative structure and quality annotations .

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Ramon Ruiz-Dolz, Debela Gemechu, Zlata Kikteva, Chris Reed

BERTweet’s TACO Fiesta: Contrasting Flavors On The Path Of Inference And Information-Driven Argument Mining On Twitter (2024.findings-naacl)

Challenge:	Argument mining is the task of automatically identifying argumentative structures in natural language documents.
Approach:	They propose to use context and semantic similarity to sample non-related propositions . argument mining is the task of automatically identifying argumentative structures in natural language documents .
Outcome:	The proposed sampling strategies improve the performance of argument mining tasks.

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Marc Feger, Stefan Dietze

Text Classification

Challenge:	Argument mining is a challenging analytical task in the rich context of Twitter (now X).
Approach:	They propose to optimize the embeddings of the BERTweet transformer for argument mining on Twitter and broader generalization across topics.
Outcome:	The proposed approach improves classification and generalization across topics using a siamese network and a dataset.

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)

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Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

Information Extraction & Text Mining

Challenge:	Argument mining (AM) is a computational process that is used to analyze information in a debating system.
Approach:	They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks .
Outcome:	The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks .

A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (N19-1)

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Tristan Miller, Maria Sukhareva, Iryna Gurevych

Annotating Arguments in a Corpus of Opinion Articles (2022.lrec-1)

Challenge:	Existing methods for analyzing discourse-level argument annotations require expensive labor and data.
Approach:	They propose a method that breaks down a popular but complex discourse-level argument annotation scheme into a simple iterative procedure that can be applied even by untrained annotators.
Outcome:	The proposed method can be applied even by untrained annotators.

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Gil Rocha, Luís Trigo, Henrique Lopes Cardoso, Rui Sousa-Silva, Paula Carvalho, Bruno Martins, Miguel Won

Argument Mining

Challenge:	Argument annotation is the process of exposing and justifying one's points of view, with the aim of conveying a logical reasoning through a set of semantically related propositions.
Approach:	They propose to use argumentative discourse units to annotate arguments in Portuguese using a multi-layered process to analyze the annotations produced.
Outcome:	The proposed model exploits the best practices identified in previous studies while fostering the potential use of the resulting annotated corpus for new purposes.

End-to-End Argument Mining over Varying Rhetorical Structures (2023.findings-acl)

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Elena Chistova

Text Generation

Challenge:	Rhetorical Structure Theory implies no single discourse interpretation of a text . inconsistent parsing of similar structures can result in inconsistent argumentation analysis .
Approach:	They propose a deep dependency parsing model to assess the connection between rhetorical and argument structures.
Outcome:	The proposed model allows for end-to-end argumentation analysis using a rhetorical tree instead of a word sequence.

Argument Mining for Understanding Peer Reviews (N19-1)

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Xinyu Hua, Mitko Nikolov, Nikhil Badugu, Lu Wang

CEDAR: A Chinese Evaluation Dataset for Computational Argumentation (2026.acl-long)

Challenge:	In 2015 alone, approximately 63.4 million hours were spent on peer reviews.
Approach:	They propose to automatically detect argumentative propositions put forward by reviewers and their types by automatically detecting their types and types.
Outcome:	The proposed method detects (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement).

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Tian Lan, Jiang Li, Rong Yan, Feilong Bao, Weihua Wang, Guanglai Gao, Xiangdong Su

LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content (2021.eacl-main)

Challenge:	Existing debate datasets neglect important labels for argument mining, generation, and evaluation.
Approach:	They propose a Chinese Evaluation Dataset for Computational Argumentation that includes key arguments and key rhetorical figures, debater roles, modal words, debate results and transcripts.
Outcome:	The proposed dataset covers 600 debates about 318 topics from Chinese debate competitions.

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Shreya Gupta, Parantak Singh, Megha Sundriyal, Md. Shad Akhtar, Tanmoy Chakraborty

Responsible & Trustworthy NLP

Challenge:	Existing work on claim detection is built on the basis of a 'segregation' of claims across different domains.
Approach:	They propose a generalized generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model.
Outcome:	The proposed model outperforms baselines on six claim datasets by an average of 3 claim-F1 points and 2 claim-f1 points on the general-domain experiments.

AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)

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Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan, Kathy McKeown, Alyssa Hwang

ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences (2021.acl-long)

Challenge:	Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
Approach:	They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation.
Outcome:	The proposed model improves on existing models using pointer networks and a pre-trained language model.

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Yanjun Gao, Ting-Hao Huang, Rebecca J. Passonneau

Multimodality

Challenge:	Existing work relies on rule-based methods dependent on parsing to identify atomic sentences.
Approach:	They propose a task to decompose complex sentences into simple ones . they propose atomic clauses as atomic sentences, and a graph edit task to predict edits .
Outcome:	The proposed model performs better than baselines on MinWiki and DeSSE.

Identifying the Human Values behind Arguments (2022.acl-long)

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Johannes Kiesel, Milad Alshomary, Nicolas Handke, Xiaoni Cai, Henning Wachsmuth, Benno Stein

Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024.naacl-long)

Challenge:	et al., 2003) examines human values in natural language arguments . authors provide a dataset of 5270 arguments from four geographical cultures .
Approach:	They propose a multi-level taxonomy of human values with 54 values and a dataset of 5270 arguments from four geographical cultures, manually annotated for human values.
Outcome:	The proposed model shows that human values are more diverse than previously thought . it shows that people disagree on the best course forward on controversial issues .

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Xiao Li, Yong Jiang, Shen Huang, Pengjun Xie, Gong Cheng, Fei Huang

Structured Data in NLP

Challenge:	Existing methods for key point analysis rely on semantic similarity instead of measuring the existence of shared key points .
Approach:	They propose a key point analysis approach with pairwise generation and graph partitioning to summarize arguments into a concise set of key points.
Outcome:	The proposed model surpasses existing models on ArgKP and QAM datasets.

QT30: A Corpus of Argument and Conflict in Broadcast Debate (2022.lrec-1)

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Annette Hautli-Janisz, Zlata Kikteva, Wassiliki Siskou, Kamila Gorska, Ray Becker, Chris Reed

Dialogue Systems & Conversational Agents

Challenge:	Broadcast political debate is the public's easiest access to opinions that shape policies and enables the general public to make informed choices.
Approach:	They present the largest corpus of analysed dialogical argumentation ever created using 30 episodes of BBC's 'Question Time' from 2020 and 2021.
Outcome:	The resource is freely available at http://corpora.aifdb.org/qt30.

ESCRITO - An NLP-Enhanced Educational Scoring Toolkit (L18-1)

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Torsten Zesch, Andrea Horbach

DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays (2024.lrec-main)

Challenge:	Existing implementations are very specific to specific use cases and datasets.
Approach:	ESCRITO is a toolkit for scoring student writings using NLP techniques . authors propose teachers and NLP researchers to use APIs for scoring pipelines .
Outcome:	ESCRITO is a toolkit for scoring student writings using NLP techniques . it addresses two main user groups: teachers and NLP researchers .

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Nils-Jonathan Schaller, Andrea Horbach, Lars Ingver Höft, Yuning Ding, Jan Luca Bahr, Jennifer Meyer, Thorben Jansen

GRhOOT: Ontology of Rhetorical Figures in German (2022.lrec-1)

Challenge:	Existing corpora focus on specific out-of-school domains, such as legal documents.
Approach:	They present a digital argumentation instruction for science corpus on 4589 essays written by 1839 german secondary school students.
Outcome:	The proposed corpus is annotated according to a fine-grained annotation scheme on 4589 essays written by 1839 german secondary school students.

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Ramona Kühn, Jelena Mitrović, Michael Granitzer

HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents (2024.findings-emnlp)

Challenge:	GRhOOT is a domain ontology of rhetorical figures in the German language . the goal is to allow for easier detection of non-literal language based tasks .
Approach:	GRhOOT is a domain ontology of 110 rhetorical figures in the german language . the goal is to allow for easier detection and sentiment analysis .
Outcome:	The ontology of rhetorical figures in the German language is based on 110 rhetorical figure domains . the goal is to make the ontologies more accurate and to allow for easier detection .

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Santosh T.y.s.s, Apolline Isaia, Shiyu Hong, Matthias Grabmair

ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining (2021.acl-long)

Challenge:	Existing approaches overlook the varying difficulty levels inherent in legal document discourse styles and rhetorical roles.
Approach:	They propose a hierarchical curriculum learning framework for RRL that nests two curricula: Rhetorical Role-level Curriculum (RC) on the outer layer and Document-level curriculum (DC) on inner layer.
Outcome:	The proposed framework is based on four legal document datasets and shows that it is complementary to existing models.

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Alexander Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev

Text Generation

Challenge:	Abstractive text summarization has primarily focused on modeling news articles . lack of standardized datasets for summarizing online conversations is a major problem .
Approach:	They propose to crowdsource four new datasets for summarizing online conversations . they incorporate argument mining through graph construction to directly model issues, viewpoints, and assertions present in a conversation.
Outcome:	The proposed models are compared against widely-used conversation summarization datasets and show comparable or improved results.

Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining? (2022.acl-long)

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Subhabrata Dutta, Jeevesh Juneja, Dipankar Das, Tanmoy Chakraborty

Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration (2025.coling-main)

Challenge:	Existing methods for argument mining are limited by the scarcity of manually annotated data and the highly domain-dependent nature of argumentation.
Approach:	They propose a novel transfer learning strategy to fine tune pretrained Transformer-based Language Models on a selectively masked language modeling task and a new prompt-based strategy for inter-component relation prediction.
Outcome:	The proposed method outperforms existing models on both within- and out-of-domain datasets while leveraging on the discourse context.

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Ramona Kühn, Jelena Mitrović, Michael Granitzer

Transferring Confluent Knowledge to Argument Mining (2022.coling-1)

Challenge:	Rhetorical figures are used to convey subtle, implicit meanings or to emphasize statements.
Approach:	They propose a web application that facilitates the identification and annotation of German rhetorical figures.
Outcome:	The proposed application improves the user experience with Retrieval Augmented Generation (RAG).

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João António Rodrigues, António Branco

GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns (2022.lrec-1)

Challenge:	Argument mining is a natural language processing task that seeks to obtain structured arguments from unstructured text.
Approach:	They propose to use a transfer learning methodology to assess the potential of argument mining knowledge with confluent tasks.
Outcome:	The proposed method dispenses with heavy feature and model engineering and allows for new state-of-the-art performance for its three main sub-tasks.

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Piyawat Lertvittayakumjorn, Leshem Choshen, Eyal Shnarch, Francesca Toni

Responsible & Trustworthy NLP

Challenge:	a Python library is available for extracting patterns from textual data.
Approach:	They propose a Python library for extracting patterns from textual data . it integrates a public implementation of the existing GrASP algorithm .
Outcome:	The proposed library integrates a public implementation of the existing GrASP algorithm.

GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change (2022.lrec-1)

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Robin Schaefer, Manfred Stede

Sentiment Analysis

Challenge:	Recent work on annotated resources focused on single argument components, i.e., claim or evidence.
Approach:	They propose to annotate a German climate change argument corpus using sarcasm and toxic language to facilitate filtering out non-argumentative content.
Outcome:	The proposed corpus is the first to be annotated for argumentation, sarcasm and toxic language.

Incorporating Zoning Information into Argument Mining from Biomedical Literature (2022.lrec-1)

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Boyang Liu, Viktor Schlegel, Riza Batista-Navarro, Sophia Ananiadou

A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction (2022.emnlp-main)

Challenge:	Argumentative zoning is a text zonation scheme that is used to segment text into zones that serve distinct functions.
Approach:	They propose to use zoning information to incorporate into argument mining tasks . they add zonation labels predicted by an off-the-shelf model to the beginning of each sentence .
Outcome:	The proposed models improve argument mining models without additional annotation cost.

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Lida Shi, Fausto Giunchiglia, Rui Song, Daqian Shi, Tongtong Liu, Xiaolei Diao, Hao Xu