Abstract
Within the financial communication domain, Earnings Conference Calls (ECCs) play a pivotal role in tracing (a) the presentational strategies and trust-building devices used by company representatives and (b) the relevant hot-topics for stakeholders, from which they form an (e)valuation of the company. Due to their formally regulated nature, ECCs are a favoured domain for the study of argumentation in context and the extraction of Argumentative Discourse Units (ADUs). However, the idiosyncratic structure of dialogical exchanges in Q&A sessions of ECCs, particularly at the level of question formulation, challenges existing models of argument mining, which assume adjacency of related question and answer turns in the dialogue. Maximal Interrogative Units (MIUs) are a novel approach to grouping together topically contiguous argumentative components within a question turn. MIU identification allows application of existing argument mining techniques to a less noisy unit of text, following removal of discourse regulators and splitting into sub-units of thematically related text. Evaluation of an automated method for MIU recognition is also presented with respect to gold-standard manual annotation.- Anthology ID:
- 2024.lrec-main.1265
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 14524–14530
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1265
- DOI:
- Cite (ACL):
- Giulia D’Agostino, Chris A. Reed, and Daniele Puccinelli. 2024. Segmentation of Complex Question Turns for Argument Mining: A Corpus-based Study in the Financial Domain. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14524–14530, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Segmentation of Complex Question Turns for Argument Mining: A Corpus-based Study in the Financial Domain (D’Agostino et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1265.pdf