  To alleviate the problem of structured databases' limited coverage, recent
task-oriented dialogue systems incorporate external unstructured knowledge to
guide the generation of system responses. However, these usually use word or
sentence level similarities to detect the relevant knowledge context, which
only partially capture the topical level relevance. In this paper, we examine
how to better integrate topical information in knowledge grounded task-oriented
dialogue and propose ``Topic-Aware Response Generation'' (TARG), an end-to-end
response generation model. TARG incorporates multiple topic-aware attention
mechanisms to derive the importance weighting scheme over dialogue utterances
and external knowledge sources towards a better understanding of the dialogue
history. Experimental results indicate that TARG achieves state-of-the-art
performance in knowledge selection and response generation, outperforming
previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4
respectively on Doc2Dial, and performing comparably with previous work on
DSTC9; both being knowledge-grounded task-oriented dialogue datasets.
