Transferring Confluent Knowledge to Argument Mining

João António Rodrigues, António Branco


Abstract
Relevant to all application domains where it is important to get at the reasons underlying sentiments and decisions, argument mining seeks to obtain structured arguments from unstructured text and has been addressed by approaches typically involving some feature and/or neural architecture engineering. By adopting a transfer learning methodology, and by means of a systematic study with a wide range of knowledge sources promisingly suitable to leverage argument mining, the aim of this paper is to empirically assess the potential of transferring such knowledge learned with confluent tasks. By adopting a lean approach that dispenses with heavier feature and model engineering, this study permitted both to gain novel empirically based insights into the argument mining task and to establish new state of the art levels of performance for its three main sub-tasks, viz. identification of argument components, classification of the components, and determination of the relation among them.
Anthology ID:
2022.coling-1.597
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6859–6874
Language:
URL:
https://aclanthology.org/2022.coling-1.597
DOI:
Bibkey:
Cite (ACL):
João António Rodrigues and António Branco. 2022. Transferring Confluent Knowledge to Argument Mining. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6859–6874, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Transferring Confluent Knowledge to Argument Mining (Rodrigues & Branco, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.597.pdf
Code
 nlx-group/transfer-am
Data
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