  Although neural models have achieved competitive results in dialogue systems,
they have shown limited ability in representing core semantics, such as
ignoring important entities. To this end, we exploit Abstract Meaning
Representation (AMR) to help dialogue modeling. Compared with the textual
input, AMR explicitly provides core semantic knowledge and reduces data
sparsity. We develop an algorithm to construct dialogue-level AMR graphs from
sentence-level AMRs and explore two ways to incorporate AMRs into dialogue
systems. Experimental results on both dialogue understanding and response
generation tasks show the superiority of our model. To our knowledge, we are
the first to leverage a formal semantic representation into neural dialogue
modeling.
