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.
@article{arxiv.1904.05606,
title = {Multi-lingual Dialogue Act Recognition with Deep Learning Methods},
author = {Jiří Martínek and Pavel Král and Ladislav Lenc and Christophe Cerisara},
journal= {arXiv preprint arXiv:1904.05606},
year = {2019}
}