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End-to-End task-oriented dialogue systems generate responses based on dialoghistory and an accompanying knowledge base (KB). Inferring those KB entitiesthat are most relevant for an utterance is crucial for response generation.Existing state of the art scales to large KBs by softly filtering overirrelevant KB information. In this paper, we propose a novel filteringtechnique that consists of (1) a pairwise similarity based filter thatidentifies relevant information by respecting the n-ary structure in a KBrecord. and, (2) an auxiliary loss that helps in separating contextuallyunrelated KB information. We also propose a new metric -- multiset entity F1which fixes a correctness issue in the existing entity F1 metric. Experimentalresults on three publicly available task-oriented dialog datasets show that ourproposed approach outperforms existing state-of-the-art models.

Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs