Deep Active Learning for Dialogue Generation
Nabiha Asghar, Pascal Poupart, Xin Ran Jiang, Hang Li · 2017
We propose an online, end-to-end, neural generative conversational model for opendomain dialogue.It is trained using a unique combination of offline two-phase supervised learning and online human-inthe-loop active learning.While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character userfeedback at each step.Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.