Dialogue topic tracking is a sequential labelling problem of recognizing the topic state at each time step in given dialogue sequences. This paper presents various artificial neural network models for dialogue topic tracking, including convolutional neural networks to account for semantics at each individual utterance, and recurrent neural networks to account for conversational contexts along multiple turns in the dialogue history. The experimental results demonstrate that our proposed models can significantly improve the tracking performances in human-human conversations.
REF build a neural network for dialogue topic tracking where the CNN used to account for semantics at individual utterance and RNN for modeling conversational contexts along multiple turns in history.
Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking
ACL
Computer Science