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        <title>Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game</title>
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    <name type="personal">
        <namePart type="given">Haichao</namePart>
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        <namePart type="given">Haonan</namePart>
        <namePart type="family">Yu</namePart>
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    <name type="personal">
        <namePart type="given">Wei</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
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    <abstract>Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1243</identifier>
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%0 Conference Proceedings
%T Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game
%A Zhang, Haichao
%A Yu, Haonan
%A Xu, Wei
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhang-etal-2018-interactive
%X Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.
%R 10.18653/v1/P18-1243
%U https://aclanthology.org/P18-1243
%U https://doi.org/10.18653/v1/P18-1243
%P 2609-2619

Markdown (Informal)

[Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game](https://aclanthology.org/P18-1243) (Zhang et al., ACL 2018)

• Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game (Zhang et al., ACL 2018)

ACL

• Haichao Zhang, Haonan Yu, and Wei Xu. 2018. Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2609–2619, Melbourne, Australia. Association for Computational Linguistics.