Dialogue-Act Prediction of Future Responses Based on Conversation History
Koji Tanaka, Jun‐ya Takayama, Yuki Arase · 2019
Sequence-to-sequence models are a common approach to develop a chatbot.They can train a conversational model in an end-to-end manner.One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear.To tackle this problem, an interpretable response generation mechanism is desired.As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process.In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs.Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8% higher F1-score and 3.0% higher accuracy on DA prediction.