Papers with encoder-decoders -
Retrieval Augmentation Reduces Hallucination in Conversation (2021.findings-emnlp)
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| Challenge: | State-of-the-art dialogue models suffer from factual incorrectness and hallucination of knowledge. |
| Approach: | They propose to use neural-retrieval-in-the-loop architectures to optimize knowledge-grounded dialogue by retrieving, ranking, and encoder-decoders. |
| Outcome: | The proposed architectures exhibit open-domain conversational capabilities and generalize effectively to scenarios not within the training data. |