Code-switched inspired losses for generic spoken dialog representations

• URL: http://arxiv.org/abs/2108.12465v1
• Date: Fri, 27 Aug 2021 19:15:00 GMT
• Title: Code-switched inspired losses for generic spoken dialog representations
• Authors: Emile Chapuis, Pierre Colombo, Matthieu Labeau, Chloe Clave
• Abstract summary: 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. Our experiments show that our new code switched-inspired losses achieve a better performance in both monolingual and multilingual settings.
• Score: 5.73995120847626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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