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This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences.
The identification of Dialogue Act’s (DA) is an important aspect in determining the meaning of an utterance for many applications that require natural language understanding, and recent work using recurrent neural networks (RNN) has shown promising results when applied to the DA classification problem.
Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications.
Ranked #1 on Dialogue Act Classification on Switchboard corpus
Therefore it is a useful technique for tuning ANN models to yield the best performances for natural language processing tasks.