Probabilistic word association for dialogue act classification with recurrent neural networks

Duran, Nathan; Battle, Steve

Abstract

The identification of Dialogue Acts (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. This work presents a novel probabilistic method of utterance representation and describes a RNN sentence model for out-of-context DA Classification.The utterance representations are generated from keywords selected for their frequency association with certain DAs. The proposed probabilistic representations are applied to the Switchboard DA corpus and performance is compared with pre-trained word embeddings using the same baseline RNN model. The results indicate that the probabilistic method achieves 75.48% overall accuracy and an improvement over the word embedding representations of 1.8%. This demonstrates the potential utility of using statistical utterance representations, that are able to capture word-DA relationships,
for the purpose of DA classification.

Citation

Duran, N., & Battle, S. (2018). Probabilistic word association for dialogue act classification with recurrent neural networks. In G. Boracchi, L. Iliadis, C. Jayne, & A. Likas (Eds.), Engineering Applications of Neural Networks (229-239). Springer

• Conference Name: 19th International Conference, EANN
• Conference Location: Bristol
• Acceptance Date: Jun 1, 2018
• Publication Date: Apr 20, 2018
• Deposit Date: Sep 21, 2018
• Publicly Available Date: Sep 21, 2018
• Peer Reviewed: Peer Reviewed
• Pages: 229-239
• Series Title: Communications in Computer and Information Science
• Book Title: Engineering Applications of Neural Networks
• ISBN: 9783319651729
• Public URL: https://uwe-repository.worktribe.com/output/1434940
• Publisher URL: http://dx.doi.org/10.1007/978-3-319-65172-9
• Additional Information: Title of Conference or Conference Proceedings : Engineering Applications of Neural Networks