LSTM for Dialogue Breakdown Detection: Exploration of Different Model Types and Word Embeddings

Abstract

One of the principal problems of human-computer interaction is miscommunication. Occurring mainly on behalf of the dialogue system, miscommunication can lead to dialogue breakdown, i.e., a point when the dialogue cannot be continued. Detecting breakdown can facilitate its prevention or recovery after breakdown occurred. In the paper, we propose a multinomial sequence classifier for dialogue breakdown detection. We explore several LSTM models each different in terms of model type and word embedding models they use. We select our best performing model and compare it with the performance of the best model and with the majority baseline from the previous challenge. We conclude that our detector outperforms the baselines during the offline testing.