  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.
