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    <titleInfo>
        <title>Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain</title>
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    <name type="personal">
        <namePart type="given">Mingming</namePart>
        <namePart type="family">Sun</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain. Then, we develop an agent, called Orator, to accomplish the OIN task, and assemble the Orator and the recently proposed OIE agent — Logician into a dual system to utilize the duality structure with a reinforcement learning paradigm. Experimental results reveal the dual structure between OIE and OIN tasks helps to build better both OIE agents and OIN agents.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1236</identifier>
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        <url>https://aclanthology.org/D18-1236</url>
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    <part>
        <date>2018-oct-nov</date>
        <extent unit="page">
            <start>2119</start>
            <end>2130</end>
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%0 Conference Proceedings
%T Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain
%A Sun, Mingming
%A Li, Xu
%A Li, Ping
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F sun-etal-2018-logician
%X We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain. Then, we develop an agent, called Orator, to accomplish the OIN task, and assemble the Orator and the recently proposed OIE agent — Logician into a dual system to utilize the duality structure with a reinforcement learning paradigm. Experimental results reveal the dual structure between OIE and OIN tasks helps to build better both OIE agents and OIN agents.
%R 10.18653/v1/D18-1236
%U https://aclanthology.org/D18-1236
%U https://doi.org/10.18653/v1/D18-1236
%P 2119-2130

Markdown (Informal)

[Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain](https://aclanthology.org/D18-1236) (Sun et al., EMNLP 2018)

• Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (Sun et al., EMNLP 2018)

ACL

• Mingming Sun, Xu Li, and Ping Li. 2018. Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2119–2130, Brussels, Belgium. Association for Computational Linguistics.