Image Retrieval for Arguments Using Stance-Aware Query Expansion

Johannes Kiesel, Nico Reichenbach, Benno Stein, Martin Potthast


Abstract
Many forms of argumentation employ images as persuasive means, but research in argument mining has been focused on verbal argumentation so far. This paper shows how to integrate images into argument mining research, specifically into argument retrieval. By exploiting the sophisticated image representations of keyword-based image search, we propose to use semantic query expansion for both the pro and the con stance to retrieve “argumentative images” for the respective stance. Our results indicate that even simple expansions provide a strong baseline, reaching a precision@10 of 0.49 for images being (1) on-topic, (2) argumentative, and (3) on-stance. An in-depth analysis reveals a high topic dependence of the retrieval performance and shows the need to further investigate on images providing contextual information.
Anthology ID:
2021.argmining-1.4
Volume:
Proceedings of the 8th Workshop on Argument Mining
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
ArgMining | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
36–45
Language:
URL:
https://aclanthology.org/2021.argmining-1.4
DOI:
10.18653/v1/2021.argmining-1.4
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.argmining-1.4.pdf