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Lexical Knowledge Internalization for Neural Dialog Generation
[article]
2022
arXiv
pre-print
We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models. Instead of further conditioning the knowledge-grounded dialog (KGD) models on externally retrieved knowledge, we seek to integrate knowledge about each input token internally into the model's parameters. To tackle the challenge due to the large scale of lexical knowledge, we adopt the contrastive learning approach and create an effective token-level lexical knowledge retriever
arXiv:2205.01941v1
fatcat:dmxt56prungith4njknjwubjda