17th AIAI 2021, 25 - 27 June 2021, Greece

Cross-lingual Approaches for Task-specific Dialogue ActRecognition

Jiří Martínek, Christophe Cerisara, Pavel Kral, Ladislav Lenc


  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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