Cross-lingual Approaches for Task-specific Dialogue Act Recognition

• URL: http://arxiv.org/abs/2005.09260v2
• Date: Wed, 21 Apr 2021 06:27:08 GMT
• Title: Cross-lingual Approaches for Task-specific Dialogue Act Recognition
• Authors: Ji\v{r}\'i Mart\'inek, Christophe Cerisara, Pavel Kr\'al and Ladislav Lenc
• Abstract summary: We exploit cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations. We design a transfer learning approach for dialogue act recognition and validate it on two different target languages and domains.
• Score: 1.8352113484137629
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper we exploit cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations. We design a transfer learning approach for dialogue act recognition and validate it on two different target languages and domains. We compute dialogue turn embeddings with both a CNN and multi-head self-attention model and show that the best results are obtained by combining all sources of transferred information. We further demonstrate that the proposed methods significantly outperform related cross-lingual DA recognition approaches.

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