Dialogue Breakdown Detection using Long Short-Term Memory Recurrent Neural Network

Abstract

Thip paper describes a method for dialogue breakdown detection using recurrent neural network with long short-term memory cells (LSTM-RNN). The proposed method uses a pair of system's utterance and preceding user's utterance for dialogue breakdown detection. Each utterances are converted into sequences of vector representation of word by word2vec and we use it for the input of the LSTM-RNN. In our model, we build two LSTM-RNNs, for processing user's utterances and system's uttearnces. The sequences of user's utterance and system's utterance are processed by each LSTM-RNNs and our model estimates distributions of annotations of dialogue breakdown by integrating each outputs. Experimenal results show that the proposed methods outperform the baseline method in detection of X and estimation of annotation distribution. However, in detection of [] and X, the performances of our methods are lower then the baseline method.