Multi-Lingual Dialogue Act Recognition with Deep Learning Methods

Jiří Martínek and Pavel Král and Ladislav Lenc and Christophe Cerisara
Interspeech 2019 (2019)
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Research topics

Dialogue act recognition | Neural networks


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.

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