Contextual Argument Component Classification for Class Discussions

Luca Lugini, Diane Litman


Abstract
Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models. In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. We find that both context types can improve performance, although the improvements are dependent on context size and position.
Anthology ID:
2020.coling-main.128
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1475–1480
Language:
URL:
https://aclanthology.org/2020.coling-main.128
DOI:
10.18653/v1/2020.coling-main.128
Bibkey:
Cite (ACL):
Luca Lugini and Diane Litman. 2020. Contextual Argument Component Classification for Class Discussions. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1475–1480, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Contextual Argument Component Classification for Class Discussions (Lugini & Litman, COLING 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.128.pdf