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Dialogue History Integration into End-to-End Signal-to-Concept Spoken Language Understanding Systems

Abstract : This work investigates the embeddings for representing dialog history in spoken language understanding (SLU) systems. We focus on the scenario when the semantic information is extracted directly from the speech signal by means of a single end-to-end neural network model. We proposed to integrate dialogue history into an end-to-end signal-to-concept SLU system. The dialog history is represented in the form of dialog history embedding vectors (so-called h-vectors) and is provided as an additional information to end-to-end SLU models in order to improve the system performance. Three following types of h-vectors are proposed and experimentally evaluated in this paper: (1) supervised-all embeddings predicting bag-of-concepts expected in the answer of the user from the last dialog system response; (2) supervised-freq embeddings focusing on predicting only a selected set of semantic concept (corresponding to the most frequent errors in our experiments); and (3) unsupervised embeddings. Experiments on the MEDIA corpus for the semantic slot filling task demonstrate that the proposed h-vectors improve the model performance.
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Contributor : Natalia Tomashenko Connect in order to contact the contributor
Submitted on : Thursday, April 23, 2020 - 10:09:16 AM
Last modification on : Thursday, September 2, 2021 - 3:28:07 AM

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Natalia Tomashenko, Christian Raymond, Antoine Caubrière, Renato de Mori, Yannick Estève. Dialogue History Integration into End-to-End Signal-to-Concept Spoken Language Understanding Systems. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2020, Barcelona, Spain. pp.5, ⟨10.1109/ICASSP40776.2020.9053247⟩. ⟨hal-02551760⟩



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