Abstract
We introduce a concept of a virtual discourse tree to improve question answering (Q/A) recall for complex, multi-sentence questions. Augmenting the discourse tree of an answer with tree fragments obtained from text corpora playing the role of ontology, we obtain on the fly a canonical discourse representation of this answer that is independent of the thought structure of a given author. This mechanism is critical for finding an answer that is not only relevant in terms of questions entities but also in terms of inter-relations between these entities in an answer and its style. We evaluate the Q/A system enabled with virtual discourse trees and observe a substantial increase of performance answering complex questions such as Yahoo! Answers and www.2carpros.com.- Anthology ID:
- R19-1044
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
- Month:
- September
- Year:
- 2019
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 373–381
- Language:
- URL:
- https://aclanthology.org/R19-1044
- DOI:
- 10.26615/978-954-452-056-4_044
- Cite (ACL):
- Boris Galitsky and Dmitry Ilvovsky. 2019. Discourse-Based Approach to Involvement of Background Knowledge for Question Answering. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 373–381, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Discourse-Based Approach to Involvement of Background Knowledge for Question Answering (Galitsky & Ilvovsky, RANLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/R19-1044.pdf