Argument Mining
85 papers with code • 1 benchmarks • 6 datasets
Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
Source: AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Benchmarks
These leaderboards are used to track progress in Argument Mining
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UKP
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Subtasks
Latest papers with no code
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources
Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations.
Multi-Task Learning Improves Performance In Deep Argument Mining Models
The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis.
Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?
We demonstrate the impact of our approach on an Argument Mining downstream task, evaluated on different corpora, showing that language models can be trained to automatically fill in discourse markers across different corpora, improving the performance of a downstream model in some, but not all, cases.
Argument Mining using BERT and Self-Attention based Embeddings
Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments.
Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents
In this work, we reformulate the task at span level as identifying spans of multiple consecutive sentences that share the same rhetorical role label to be assigned via classification.
Toward an Intelligent Tutoring System for Argument Mining in Legal Texts
We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user.
Multi-granularity Argument Mining in Legal Texts
In this work, we conceptualize argument mining as a token-level (i. e., word-level) classification problem.
Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction
This paper describes our contributions to the Shared Task of the 9th Workshop on Argument Mining (2022).
Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification
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.
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments
The task of argument mining aims to detect all possible argumentative components and identify their relationships automatically.
