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Pre-trained language models have made great progress on dialogue tasks.However, these models are typically trained on surface dialogue text, thus areproven to be weak in understanding the main semantic meaning of a dialoguecontext. We investigate Abstract Meaning Representation (AMR) as explicitsemantic knowledge for pre-training models to capture the core semanticinformation in dialogues during pre-training. In particular, we propose asemantic-based pre-training framework that extends the standard pre-trainingframework (Devlin et al., 2019) by three tasks for learning 1) core semanticunits, 2) semantic relations and 3) the overall semantic representationaccording to AMR graphs. Experiments on the understanding of both chit-chatsand task-oriented dialogues show the superiority of our model. To ourknowledge, we are the first to leverage a deep semantic representation fordialogue pre-training.

Semantic-based Pre-training for Dialogue Understanding