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        <title>Concept Transfer Learning for Adaptive Language Understanding</title>
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            <title>Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue</title>
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            <namePart type="given">Kazunori</namePart>
            <namePart type="family">Komatani</namePart>
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    <abstract>Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated. To address this issue, in this paper, a novel concept transfer learning approach is proposed. Here, substructures within literal concept definition are investigated to reveal the relationship between concepts. A hierarchical semantic representation for concepts is proposed, where a semantic slot is represented as a composition of atomic concepts. Based on this new hierarchical representation, transfer learning approaches are developed for adaptive LU. The approaches are applied to two tasks: value set mismatch and domain adaptation, and evaluated on two LU benchmarks: ATIS and DSTC 2&amp;3. Thorough empirical studies validate both the efficiency and effectiveness of the proposed method. In particular, we achieve state-of-the-art performance (F₁-score 96.08%) on ATIS by only using lexicon features.</abstract>
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    <identifier type="doi">10.18653/v1/W18-5047</identifier>
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%0 Conference Proceedings
%T Concept Transfer Learning for Adaptive Language Understanding
%A Zhu, Su
%A Yu, Kai
%Y Komatani, Kazunori
%Y Litman, Diane
%Y Yu, Kai
%Y Papangelis, Alex
%Y Cavedon, Lawrence
%Y Nakano, Mikio
%S Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhu-yu-2018-concept
%X Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated. To address this issue, in this paper, a novel concept transfer learning approach is proposed. Here, substructures within literal concept definition are investigated to reveal the relationship between concepts. A hierarchical semantic representation for concepts is proposed, where a semantic slot is represented as a composition of atomic concepts. Based on this new hierarchical representation, transfer learning approaches are developed for adaptive LU. The approaches are applied to two tasks: value set mismatch and domain adaptation, and evaluated on two LU benchmarks: ATIS and DSTC 2&3. Thorough empirical studies validate both the efficiency and effectiveness of the proposed method. In particular, we achieve state-of-the-art performance (F₁-score 96.08%) on ATIS by only using lexicon features.
%R 10.18653/v1/W18-5047
%U https://aclanthology.org/W18-5047
%U https://doi.org/10.18653/v1/W18-5047
%P 391-399

Markdown (Informal)

[Concept Transfer Learning for Adaptive Language Understanding](https://aclanthology.org/W18-5047) (Zhu & Yu, SIGDIAL 2018)

• Concept Transfer Learning for Adaptive Language Understanding (Zhu & Yu, SIGDIAL 2018)

ACL

• Su Zhu and Kai Yu. 2018. Concept Transfer Learning for Adaptive Language Understanding. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 391–399, Melbourne, Australia. Association for Computational Linguistics.