| Challenge: | Argument(ation) mining is a task of identifying argument structure from text . lack of training data makes it difficult to train models based on limited data sets. |
| Approach: | They propose an end-to-end cross-corpus argument mining method that uses auxiliary argument mining corpora to train models. |
| Outcome: | The proposed method outperforms models trained on a single corpus on arguments on arguments in argument mining tasks. |
Similar Papers
Multi-Task Learning for Argumentation Mining in Low-Resource Settings (N18-2)
Copied to clipboard
| Challenge: | Argument component identification is difficult for trained annotators to perform in a new domain or to develop new AM tasks. |
| Approach: | They investigate whether multi-task learning can improve performance on AM problems . they found that MTL performs particularly well when little training data is available for the main task . |
| Outcome: | The proposed approach performs better when little training data is available for the main task, a common scenario in AM. |
A Neural Transition-based Model for Argumentation Mining (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for identifying argumentation structures are inefficient and class imbalanced. |
| Approach: | They propose a neural transition-based model that incrementally builds an argumentation graph by generating a sequence of actions. |
| Outcome: | The proposed model can handle tree and non-tree structured argumentation without structural constraints. |
Argument Mining as a Text-to-Text Generation Task (2024.eacl-long)
Copied to clipboard
| Challenge: | Argument Mining (AM) aims to uncover the argumentative structures within a text. |
| Approach: | They propose a method that generates argumentatively annotated text using a pretrained encoder-decoder language model and a pre-trained decoder. |
| Outcome: | The proposed method achieves state-of-the-art performance on three types of benchmark datasets. |
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)
Copied to clipboard
| Challenge: | Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks. |
| Approach: | They propose a generative framework where expected outputs of AM are framed as a simple target sequence. |
| Outcome: | The proposed framework achieves state-of-the-art on two AM benchmarks. |
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! (C18-1)
Copied to clipboard
| Challenge: | Argumentation mining (AM) requires the identification of complex discourse structures . existing resources are not adequate for assessing cross-lingual AM due to their heterogeneity or lack of complexity. |
| Approach: | They propose to use a dataset to translate persuasive student essays into German, French, Spanish, and Chinese to compare arguments mining and annotation projection. |
| Outcome: | The proposed methods perform better when using expensive human or cheap machine translations and almost eliminate loss from cross-lingual transfer. |
Cross-topic Argument Mining from Heterogeneous Sources (D18-1)
Copied to clipboard
| Challenge: | Argument mining is a core technology for automating argument search in document collections. |
| Approach: | They propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. |
| Outcome: | The proposed scheme outperforms vanilla BiLSTMs in two- and three-label cross-topic settings and can be further improved by leveraging additional data for topic relevance using multi-task learning. |
TARGER: Neural Argument Mining at Your Fingertips (P19-3)
Copied to clipboard
Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
| Challenge: | Argumentation is a multi-disciplinary field that extends from philosophy and psychology to linguistics as well as to artificial intelligence. |
| Approach: | They propose to use TARGER to tagging arguments in free text and keyword-based retrieval of arguments from a web-scale corpus. |
| Outcome: | The proposed framework can be used without any reproducibility effort on the user's side and is easily portable to other domains and use cases. |
End-to-End Argument Mining as Biaffine Dependency Parsing (2021.eacl-main)
Copied to clipboard
| 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. |
External Knowledge-Driven Argument Mining: Leveraging Attention-Enhanced Multi-Network Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Argument mining involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs). |
| Approach: | They propose to leverage external resources to identify semantic paths linking ADUs . they propose to use WordNet, ConceptNet, and Wikipedia to identify these paths . |
| Outcome: | The proposed architecture achieves F-scores of 0.85, 0.84, 0.70, and 0.87 on four datasets. |
Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)
Copied to clipboard
Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu
| Challenge: | Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). |
| Approach: | They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts. |
| Outcome: | The proposed framework outperforms strong baselines while significantly improving explainability. |