Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback (2023.acl-long)
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| Challenge: | Frozen models trained to mimic static datasets can never improve their performance. |
| Approach: | They propose to use binary quality measurements and free-form text feedback to improve conversational skills in a conversational learning framework. |
| Outcome: | The proposed model improves on the DIRECTOR model, which is based on binary quality measurements and free-form text feedback, and shows that iterative retraining and redeployment can improve the model. |
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| Challenge: | Xu et al., 2023) and Bai ed., 2019) use crowdworkers to collect signals from natural dialogue episodes. |
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| Challenge: | Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. |
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| Challenge: | Existing datasets for learning from free-text human feedback are scarce. |
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| Challenge: | Recent task-oriented dialogue systems are trained on annotated dialogues, but when domain knowledge changes, the initial model may become obsolete. |
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