Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation (2020.findings-emnlp)
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| Challenge: | Existing knowledge selection models are limited by the context, but the difference between selected knowledge at different turns is often overlooked. |
| Approach: | They propose a difference-aware knowledge selection method that computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. |
| Outcome: | The proposed method outperforms the state-of-the-art methods in a knowledge-grounded dialog. |
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| Challenge: | Existing conversation models treat knowledge selection as a sentence ranking problem where each sentence is handled individually, ignoring the internal semantic connection between sentences. |
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