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To alleviate the problem of structured databases' limited coverage, recenttask-oriented dialogue systems incorporate external unstructured knowledge toguide the generation of system responses. However, these usually use word orsentence level similarities to detect the relevant knowledge context, whichonly partially capture the topical level relevance. In this paper, we examinehow to better integrate topical information in knowledge grounded task-orienteddialogue and propose ``Topic-Aware Response Generation'' (TARG), an end-to-endresponse generation model. TARG incorporates multiple topic-aware attentionmechanisms to derive the importance weighting scheme over dialogue utterancesand external knowledge sources towards a better understanding of the dialoguehistory. Experimental results indicate that TARG achieves state-of-the-artperformance in knowledge selection and response generation, outperformingprevious state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4respectively on Doc2Dial, and performing comparably with previous work onDSTC9; both being knowledge-grounded task-oriented dialogue datasets.

Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access