Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation
- Resource Type
- Working Paper
- Authors
- Zhou, Mingyang; Arnold, Josh; Yu, Zhou
- Source
- Subject
- Computer Science - Computation and Language
Computer Science - Artificial Intelligence
Computer Science - Machine Learning
- Language
Reinforcement learning (RL) is an effective approach to learn an optimal dialog policy for task-oriented visual dialog systems. A common practice is to apply RL on a neural sequence-to-sequence (seq2seq) framework with the action space being the output vocabulary in the decoder. However, it is difficult to design a reward function that can achieve a balance between learning an effective policy and generating a natural dialog response. This paper proposes a novel framework that alternatively trains a RL policy for image guessing and a supervised seq2seq model to improve dialog generation quality. We evaluate our framework on the GuessWhich task and the framework achieves the state-of-the-art performance in both task completion and dialog quality.
Comment: updated with acknowledgement and minor typo fixes on tables