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Knowledge Grounded Conversation Models (KGCM) are usually based on aselection/retrieval module and a generation module, trained separately orsimultaneously, with or without having access to a gold knowledge option. Withthe introduction of large pre-trained generative models, the selection andgeneration part have become more and more entangled, shifting the focus towardsenhancing knowledge incorporation (from multiple sources) instead of trying topick the best knowledge option. These approaches however depend on knowledgelabels and/or a separate dense retriever for their best performance. In thiswork we study the unsupervised selection abilities of pre-trained generativemodels (e.g. BART) and show that by adding a score-and-aggregate module betweenencoder and decoder, they are capable of learning to pick the proper knowledgethrough minimising the language modelling loss (i.e. without having access toknowledge labels). Trained as such, our model - K-Mine - shows competitiveselection and generation performance against models that benefit from knowledgelabels and/or separate dense retriever.

Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models