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        <title>Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks</title>
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        <namePart type="given">Rajarshi</namePart>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing Memory networks to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on Spades fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 F1 points.</abstract>
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    <identifier type="doi">10.18653/v1/P17-2057</identifier>
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%0 Conference Proceedings
%T Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks
%A Das, Rajarshi
%A Zaheer, Manzil
%A Reddy, Siva
%A McCallum, Andrew
%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 das-etal-2017-question
%X Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing Memory networks to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on Spades fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 F1 points.
%R 10.18653/v1/P17-2057
%U https://aclanthology.org/P17-2057
%U https://doi.org/10.18653/v1/P17-2057
%P 358-365

Markdown (Informal)

[Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks](https://aclanthology.org/P17-2057) (Das et al., ACL 2017)

• Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks (Das et al., ACL 2017)

ACL

• Rajarshi Das, Manzil Zaheer, Siva Reddy, and Andrew McCallum. 2017. Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 358–365, Vancouver, Canada. Association for Computational Linguistics.