EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation. |
| Approach: | They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph. |
| Outcome: | The proposed model generates more informative, coherent, and natural responses than baseline models. |
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