Cross-Lingual Approaches for Task-Specific Dialogue Act Recognition
17th International Conference on Artificial Intelligence Applications and Innovations (2021)
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.
Authors
BibTex
@misc{https://doi.org/10.48550/arxiv.2005.09260, doi = {10.48550/ARXIV.2005.09260}, url = {https://arxiv.org/abs/2005.09260}, 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} }
