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Cross-Lingual Approaches for Task-Specific Dialogue Act Recognition

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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https://hal.inria.fr/hal-03287703
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Submitted on : Thursday, July 15, 2021 - 6:12:12 PM
Last modification on : Saturday, October 16, 2021 - 11:26:06 AM
Long-term archiving on: : Saturday, October 16, 2021 - 7:10:00 PM

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Jiří Martínek, Christophe Cerisara, Pavel Král, Ladislav Lenc. Cross-Lingual Approaches for Task-Specific Dialogue Act Recognition. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.232-242, ⟨10.1007/978-3-030-79150-6_19⟩. ⟨hal-03287703⟩

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