@inproceedings{khot-etal-2017-answering,
title = "Answering Complex Questions Using Open Information Extraction",
author = "Khot, Tushar and
Sabharwal, Ashish and
Clark, Peter",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2049",
doi = "10.18653/v1/P17-2049",
pages = "311--316",
abstract = "While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such knowledge has only been used to answer simple questions with retrieval-based methods. We overcome this limitation by presenting a method for reasoning with Open IE knowledge, allowing more complex questions to be handled. Using a recently proposed support graph optimization framework for QA, we develop a new inference model for Open IE, in particular one that can work effectively with multiple short facts, noise, and the relational structure of tuples. Our model significantly outperforms a state-of-the-art structured solver on complex questions of varying difficulty, while also removing the reliance on manually curated knowledge.",
}
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%0 Conference Proceedings
%T Answering Complex Questions Using Open Information Extraction
%A Khot, Tushar
%A Sabharwal, Ashish
%A Clark, Peter
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F khot-etal-2017-answering
%X While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such knowledge has only been used to answer simple questions with retrieval-based methods. We overcome this limitation by presenting a method for reasoning with Open IE knowledge, allowing more complex questions to be handled. Using a recently proposed support graph optimization framework for QA, we develop a new inference model for Open IE, in particular one that can work effectively with multiple short facts, noise, and the relational structure of tuples. Our model significantly outperforms a state-of-the-art structured solver on complex questions of varying difficulty, while also removing the reliance on manually curated knowledge.
%R 10.18653/v1/P17-2049
%U https://aclanthology.org/P17-2049
%U https://doi.org/10.18653/v1/P17-2049
%P 311-316
Markdown (Informal)
[Answering Complex Questions Using Open Information Extraction](https://aclanthology.org/P17-2049) (Khot et al., ACL 2017)
ACL
- Tushar Khot, Ashish Sabharwal, and Peter Clark. 2017. Answering Complex Questions Using Open Information Extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 311–316, Vancouver, Canada. Association for Computational Linguistics.