Multi-Lingual Dialogue Act Recognition with Deep Learning Methods

Jiří Martínek, Pavel Král, Ladislav Lenc, Christophe Cerisara

This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses one general model trained on the embeddings from all available languages. The second method trains the model on a single pivot language and a linear transformation method is used to project other languages onto the pivot language. The popular convolutional neural network and LSTM architectures with different set-ups are used as classifiers. To the best of our knowledge this is the first attempt at multi-lingual DA recognition using neural networks. The multi-lingual models are validated experimentally on two languages from the Verbmobil corpus.

 DOI: 10.21437/Interspeech.2019-1691

Cite as: Martínek, J., Král, P., Lenc, L., Cerisara, C. (2019) Multi-Lingual Dialogue Act Recognition with Deep Learning Methods. Proc. Interspeech 2019, 1463-1467, DOI: 10.21437/Interspeech.2019-1691.

  author={Jiří Martínek and Pavel Král and Ladislav Lenc and Christophe Cerisara},
  title={{Multi-Lingual Dialogue Act Recognition with Deep Learning Methods}},
  booktitle={Proc. Interspeech 2019},