再帰型ニューラルネットワークを用いた対話破綻検出と言語モデルのマルチタスク学習

抄録

For detecting dialog breakdowns, we make it as a classification problem using features of sentences in the dialog. The feature including context information will perform better on the task. Recurrent neural networks (RNN) can encode sentences to fixed-length feature vectors, which are based on previous contexts. In this paper, we propose a multi-task learning method of language modeling and detecting dialog breakdowns for RNN and entended models. We conducted comparative experiments for our various RNN models and learning methods on dataset of Dialog dreakdown detection challenge, and show that our proposed model has very high precision, low recall, high accuracy and very small mean squared error for prediction of dialog breakdown labels compared to other participants.