Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access
This addresses the challenge of generating more relevant responses in task-oriented dialogue systems by better leveraging unstructured knowledge, representing an incremental improvement over existing methods.
The paper tackles the problem of limited topical relevance in knowledge-grounded task-oriented dialogue systems by proposing TARG, a model that integrates topic-aware attention mechanisms, achieving state-of-the-art performance with improvements of 3.2, 3.6, and 4.2 points in EM, F1, and BLEU-4 on Doc2Dial.
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.