Multi-lingual Dialogue Act Recognition with Deep Learning Methods
It addresses the problem of understanding dialogue acts across languages for NLP applications, but it is incremental as it applies existing neural network methods to a new multi-lingual setting.
This paper tackles multi-lingual dialogue act recognition by proposing two deep learning models, one general and one using a pivot language with linear transformation, achieving results validated on two languages from the Verbmobil corpus.
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.