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• W2938532952 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." @default.
• W2938532952 created "2019-04-25" @default.
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• W2938532952 date "2019-04-11" @default.
• W2938532952 modified "2023-10-01" @default.
• W2938532952 title "Multi-lingual Dialogue Act Recognition with Deep Learning Methods" @default.
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