@inproceedings{galitsky-ilvovsky-2019-discourse,
title = "Discourse-Based Approach to Involvement of Background Knowledge for Question Answering",
author = "Galitsky, Boris and
Ilvovsky, Dmitry",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1044",
doi = "10.26615/978-954-452-056-4_044",
pages = "373--381",
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.",
}
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%0 Conference Proceedings
%T Discourse-Based Approach to Involvement of Background Knowledge for Question Answering
%A Galitsky, Boris
%A Ilvovsky, Dmitry
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F galitsky-ilvovsky-2019-discourse
%X 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.
%R 10.26615/978-954-452-056-4_044
%U https://aclanthology.org/R19-1044
%U https://doi.org/10.26615/978-954-452-056-4_044
%P 373-381
Markdown (Informal)
[Discourse-Based Approach to Involvement of Background Knowledge for Question Answering](https://aclanthology.org/R19-1044) (Galitsky & Ilvovsky, RANLP 2019)
ACL