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- W4285264420 abstract "We study learning from user feedback for extractive question answering by simulating feedback using supervised data. We cast the problem as contextual bandit learning, and analyze the characteristics of several learning scenarios with focus on reducing data annotation. We show that systems initially trained on few examples can dramatically improve given feedback from users on model-predicted answers, and that one can use existing datasets to deploy systems in new domains without any annotation effort, but instead improving the system on-the-fly via user feedback." @default.
- W4285264420 created "2022-07-14" @default.
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- W4285264420 date "2022-01-01" @default.
- W4285264420 modified "2023-09-26" @default.
- W4285264420 title "Simulating Bandit Learning from User Feedback for Extractive Question Answering" @default.
- W4285264420 doi "https://doi.org/10.18653/v1/2022.acl-long.355" @default.
- W4285264420 hasPublicationYear "2022" @default.
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