Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning (P18-1)
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| Challenge: | Recent work has shown that reinforcement learning (RL) can be scaled to games with large state-action spaces, achieving human-level performance or even superhuman performance. |
| Approach: | They propose to use bandit feedback to improve sequence-to-sequence learning by simulating reward signals by evaluation metrics such as BLEU, F1-score, or ROUGE. |
| Outcome: | The proposed methods improve performance even from small amounts of human feedback, pointing to a great potential for applications at larger scale. |
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Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Simon Stepputtis, Joseph Campbell, Katia Sycara
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| Challenge: | a recent study shows that counterfactual learning from human bandit feedback can improve neural semantic parsers . cost and difficulty of manually preparing large amounts of parses is a bottleneck for supervised learning . |
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| Challenge: | a large amount of labeled data is needed for fine-tuning. |
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