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Conference Papers Year : 2021

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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hal-03287703 , version 1 (15-07-2021)

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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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