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Robust and Scalable Conversational AI

Published: 20 April 2020 Publication History

Abstract

Even conversational systems have attracted a lot of attention recently, the current systems sometimes fail due to the errors from different components. This keynote talk presents following research directions for improving robustness and scalability of conversational systems: 1) we first focus on learning language embeddings specifically for spoken scenarios such as more noisy inputs during inference, and 2) secondly we extend the dialogue systems to access not only structured but unstructured knowledge and propose a novel learning framework for natural language understanding and generation on top of duality towards better scalability. The enhanced robustness and scalability shows the great potential of guiding future research directions.

References

[1]
Chao-Wei Huang and Yun-Nung Chen. 2019. Adapting Pretrained Transformer to Lattices for Spoken Language Understanding. In Proceedings of 2019 IEEE Automatic Speech Recognition and Understanding Workshop. IEEE, 845–852.
[2]
Chao-Wei Huang and Yun-Nung Chen. 2020. LEARNING ASR-ROBUST CONTEXTUALIZED EMBEDDINGS FOR SPOKEN LANGUAGE UNDERSTANDING. In Proceedings of The 45th IEEE International Conference on Acoustics, Speech, and Signal Processing.
[3]
Shang-Yu Su, Chao-Wei Huang, and Yun-Nung Chen. 2019. Dual Supervised Learning for Natural Language Understanding and Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 5472–5477.
[4]
Yi-Ting Yeh and Yun-Nung Chen. 2019. FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering. 86–90.

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        cover image ACM Conferences
        WWW '20: Companion Proceedings of the Web Conference 2020
        April 2020
        854 pages
        ISBN:9781450370240
        DOI:10.1145/3366424
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        New York, NY, United States

        Publication History

        Published: 20 April 2020

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        Author Tags

        1. conversational question answering
        2. conversational systems
        3. dialogue systmes
        4. natural language generation
        5. natural language understanding
        6. speech recognition

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        WWW '20
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        WWW '20: The Web Conference 2020
        April 20 - 24, 2020
        Taipei, Taiwan

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        Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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