Abstract
In this paper, we present principles of constructing and resolving ambiguity in implicit discourse relations. Following these principles, we created a dataset in both English and Egyptian Arabic that controls for semantic disambiguation, enabling the investigation of prosodic features in future work. In these datasets, examples are two-part sentences with an implicit discourse relation that can be ambiguously read as either causal or concessive, paired with two different preceding context sentences forcing either the causal or the concessive reading. We also validated both datasets by humans and language models (LMs) to study whether context can help humans or LMs resolve ambiguities of implicit relations and identify the intended relation. As a result, this task posed no difficulty for humans, but proved challenging for BERT/CamelBERT and ELECTRA/AraELECTRA models.- Anthology ID:
- 2023.codi-1.16
- Volume:
- Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Michael Strube, Chloe Braud, Christian Hardmeier, Junyi Jessy Li, Sharid Loaiciga, Amir Zeldes
- Venue:
- CODI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 126–144
- Language:
- URL:
- https://aclanthology.org/2023.codi-1.16
- DOI:
- 10.18653/v1/2023.codi-1.16
- Cite (ACL):
- Ahmed Ruby, Sara Stymne, and Christian Hardmeier. 2023. Unpacking Ambiguous Structure: A Dataset for Ambiguous Implicit Discourse Relations for English and Egyptian Arabic. In Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023), pages 126–144, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Unpacking Ambiguous Structure: A Dataset for Ambiguous Implicit Discourse Relations for English and Egyptian Arabic (Ruby et al., CODI 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.codi-1.16.pdf