The Web as a Knowledge-Base for Answering Complex Questions

Alon Talmor, Jonathan Berant


Abstract
Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, ComplexWebQuestions, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.
Anthology ID:
N18-1059
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
641–651
Language:
URL:
https://www.aclweb.org/anthology/N18-1059
DOI:
10.18653/v1/N18-1059
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1059.pdf
Dataset:
 N18-1059.Datasets.zip
Video:
 http://vimeo.com/276429529