Skip to Main content Skip to Navigation
Conference papers

Multi-Lingual Dialogue Act Recognition with Deep Learning Methods

Abstract : This paper deals with multilingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multilingual 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 setups are used as classifiers. To the best of our knowledge this is the first attempt at multilingual DA recognition using neural networks. The multilingual models are validated experimentally on two languages from the Verbmobil corpus.
Document type :
Conference papers
Complete list of metadatas

Cited literature [29 references]  Display  Hide  Download
Contributor : Christophe Cerisara <>
Submitted on : Friday, October 18, 2019 - 2:11:31 PM
Last modification on : Thursday, October 31, 2019 - 1:49:37 PM
Long-term archiving on: : Sunday, January 19, 2020 - 2:49:24 PM


Files produced by the author(s)



Jiří Martínek, Pavel Kral, Ladislav Lenc, Christophe Cerisara. Multi-Lingual Dialogue Act Recognition with Deep Learning Methods. Interspeech 2019, Sep 2019, Graz, Austria. ⟨10.21437/Interspeech.2019-1691⟩. ⟨hal-02319818⟩



Record views


Files downloads