Uncovering Implicit Inferences for Improved Relational Argument Mining

Ameer Saadat-Yazdi, Jeff Z. Pan, Nadin Kokciyan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Argument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations (such as attack, support, and neutral) between argumentative units is a challenging task because two units may be related to each other via implicit inferences. These inferences often rely on external commonsense knowledge to discover how one argumentative unit relates to another. State-of-the-art methods, however, rely on predefined knowledge graphs, and thus might not cover target pairs of argumentative units well. We introduce a new generative approach to finding inference chains that connect these pairs by making use of the Commonsense Transformer (COMET). We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks. Our approach significantly outperforms the state-of-the-art, by 2- 5% in F1 score, on two out of the three datasets with minor improvements on the remaining one.
Original languageEnglish
Title of host publicationProceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
EditorsAndreas Vlachos, Isabelle Augenstein
PublisherAssociation for Computational Linguistics (ACL)
Pages2484 – 2495
Number of pages12
Publication statusPublished - 2 May 2023
EventThe 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023 - Dubrovnik, Croatia
Duration: 2 May 20236 May 2023
Conference number: 17
https://2023.eacl.org/

Conference

ConferenceThe 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023
Abbreviated titleEACL 2023
Country/TerritoryCroatia
CityDubrovnik
Period2/05/236/05/23
Internet address

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