Discourse-Based Approach to Involvement of Background Knowledge for Question Answering

Boris Galitsky, Dmitry Ilvovsky


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
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
373–381
Language:
URL:
https://www.aclweb.org/anthology/R19-1044
DOI:
10.26615/978-954-452-056-4_044
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1044.pdf