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- W2970102153 abstract "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." @default.
- W2970102153 created "2019-09-05" @default.
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- W2970102153 date "2019-01-01" @default.
- W2970102153 modified "2023-10-16" @default.
- W2970102153 title "Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation" @default.
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- W2970102153 doi "https://doi.org/10.18653/v1/d19-1014" @default.
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