Cross-Lingual Approaches for Task-Specific Dialogue Act Recognition

Jiří Martínek and Christophe Cerisara and Pavel Král and Ladislav Lenc
17th International Conference on Artificial Intelligence Applications and Innovations (2021)



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.



@misc{, doi = {10.48550/ARXIV.2005.09260}, url = {}, author = {Martínek, Jiří and Cerisara, Christophe and Král, Pavel and Lenc, Ladislav}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Cross-lingual Approaches for Task-specific Dialogue Act Recognition}, publisher = {arXiv}, year = {2020}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} }
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