  Unstructured documents serving as external knowledge of the dialogues help to
generate more informative responses. Previous research focused on knowledge
selection (KS) in the document with dialogue. However, dialogue history that is
not related to the current dialogue may introduce noise in the KS processing.
In this paper, we propose a Compare Aggregate Transformer (CAT) to jointly
denoise the dialogue context and aggregate the document information for
response generation. We designed two different comparison mechanisms to reduce
noise (before and during decoding). In addition, we propose two metrics for
evaluating document utilization efficiency based on word overlap. Experimental
results on the CMUDoG dataset show that the proposed CAT model outperforms the
state-of-the-art approach and strong baselines.
