# Multi-lingual Dialogue Act Recognition with Deep Learning Methods

## TL;DR

This paper introduces novel deep learning models for multi-lingual dialogue act recognition, utilizing word embeddings and neural network architectures, validated on Verbmobil corpus data, marking the first such neural network application in this domain.

## Contribution

It presents two innovative multi-lingual neural network models for dialogue act recognition, including a general model and a pivot language approach, using word2vec embeddings.

## Key findings

- Models achieve effective multi-lingual DA recognition
- First neural network application for multi-lingual DA recognition
- Validated on Verbmobil corpus with promising results

## Abstract

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.