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        <title>Pattern-revising Enhanced Simple Question Answering over Knowledge Bases</title>
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        <namePart type="given">Yanchao</namePart>
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        <namePart type="given">Kang</namePart>
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        <namePart type="given">Jun</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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    <abstract>Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks. Simple questions constitute a large part of questions queried on the web, still being a challenge to QA systems. In this work, we propose to conduct pattern extraction and entity linking first, and put forward pattern revising procedure to mitigate the error propagation problem. In order to learn to rank candidate subject-predicate pairs to enable the relevant facts retrieval given a question, we propose to do joint fact selection enhanced by relation detection. Multi-level encodings and multi-dimension information are leveraged to strengthen the whole procedure. The experimental results demonstrate that our approach sets a new record in this task, outperforming the current state-of-the-art by an absolute large margin.</abstract>
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        <url>https://aclanthology.org/C18-1277</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
%A Hao, Yanchao
%A Liu, Hao
%A He, Shizhu
%A Liu, Kang
%A Zhao, Jun
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F hao-etal-2018-pattern
%X Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks. Simple questions constitute a large part of questions queried on the web, still being a challenge to QA systems. In this work, we propose to conduct pattern extraction and entity linking first, and put forward pattern revising procedure to mitigate the error propagation problem. In order to learn to rank candidate subject-predicate pairs to enable the relevant facts retrieval given a question, we propose to do joint fact selection enhanced by relation detection. Multi-level encodings and multi-dimension information are leveraged to strengthen the whole procedure. The experimental results demonstrate that our approach sets a new record in this task, outperforming the current state-of-the-art by an absolute large margin.
%U https://aclanthology.org/C18-1277
%P 3272-3282

Markdown (Informal)

[Pattern-revising Enhanced Simple Question Answering over Knowledge Bases](https://aclanthology.org/C18-1277) (Hao et al., COLING 2018)

• Pattern-revising Enhanced Simple Question Answering over Knowledge Bases (Hao et al., COLING 2018)

ACL

• Yanchao Hao, Hao Liu, Shizhu He, Kang Liu, and Jun Zhao. 2018. Pattern-revising Enhanced Simple Question Answering over Knowledge Bases. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3272–3282, Santa Fe, New Mexico, USA. Association for Computational Linguistics.