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arXiv · 2108.12465

Code-switched inspired losses for generic spoken dialog representations

Emile Chapuis·Pierre Colombo·Matthieu Labeau·Chloe Clavel
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Abstract

Spoken dialog systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introduce new pretraining losses tailored to learn multilingual spoken dialog representations. The goal of these losses is to expose the model to code-switched language. To scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from \texttt{OpenSubtitles}, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on \texttt{MIAM}, a new benchmark composed of five dialog act corpora on the same aforementioned languages as well as on two novel multilingual downstream tasks (\textit{i.e} multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new code switched-inspired losses achieve a better performance in both monolingual and multilingual settings.

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Emile Chapuis, Pierre Colombo, Matthieu Labeau, Chloe Clavel. 2021-08-27. Code-switched inspired losses for generic spoken dialog representations. https://arxiv.org/abs/2108.12465

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