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        <title>A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling</title>
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    <name type="personal">
        <namePart type="given">Haihong</namePart>
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        <namePart type="given">Peiqing</namePart>
        <namePart type="family">Niu</namePart>
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    <name type="personal">
        <namePart type="given">Zhongfu</namePart>
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    <name type="personal">
        <namePart type="given">Meina</namePart>
        <namePart type="family">Song</namePart>
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        <dateIssued>2019-07</dateIssued>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>A spoken language understanding (SLU) system includes two main tasks, slot filling (SF) and intent detection (ID). The joint model for the two tasks is becoming a tendency in SLU. But the bi-directional interrelated connections between the intent and slots are not established in the existing joint models. In this paper, we propose a novel bi-directional interrelated model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks to help them promote each other mutually. Besides, we design an entirely new iteration mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the relative improvement in the sentence-level semantic frame accuracy of our model is 3.79% and 5.42% on ATIS and Snips datasets, respectively, compared to the state-of-the-art model.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1544</identifier>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
%A E, Haihong
%A Niu, Peiqing
%A Chen, Zhongfu
%A Song, Meina
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F e-etal-2019-novel
%X A spoken language understanding (SLU) system includes two main tasks, slot filling (SF) and intent detection (ID). The joint model for the two tasks is becoming a tendency in SLU. But the bi-directional interrelated connections between the intent and slots are not established in the existing joint models. In this paper, we propose a novel bi-directional interrelated model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks to help them promote each other mutually. Besides, we design an entirely new iteration mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the relative improvement in the sentence-level semantic frame accuracy of our model is 3.79% and 5.42% on ATIS and Snips datasets, respectively, compared to the state-of-the-art model.
%R 10.18653/v1/P19-1544
%U https://aclanthology.org/P19-1544
%U https://doi.org/10.18653/v1/P19-1544
%P 5467-5471

Markdown (Informal)

[A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://aclanthology.org/P19-1544) (E et al., ACL 2019)

• A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling (E et al., ACL 2019)

ACL

• Haihong E, Peiqing Niu, Zhongfu Chen, and Meina Song. 2019. A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5467–5471, Florence, Italy. Association for Computational Linguistics.