The challenges of building knowledge-grounded retrieval-based chatbots lie in
how to ground a conversation on its background knowledge and how to match
response candidates with both context and knowledge simultaneously. This paper
proposes a method named Filtering before Iteratively REferring (FIRE) for this
task. In this method, a context filter and a knowledge filter are first built,
which derive knowledge-aware context representations and context-aware
knowledge representations respectively by global and bidirectional attention.
Besides, the entries irrelevant to the conversation are discarded by the
knowledge filter. After that, iteratively referring is performed between
context and response representations as well as between knowledge and response
representations, in order to collect deep matching features for scoring
response candidates. Experimental results show that FIRE outperforms previous
methods by margins larger than 2.8% and 4.1% on the PERSONA-CHAT dataset with
original and revised personas respectively, and margins larger than 3.1% on the
CMU_DoG dataset in terms of top-1 accuracy. We also show that FIRE is more
interpretable by visualizing the knowledge grounding process.

本文提出了一种名为 FIRE 的方法，通过上下文过滤和知识过滤构建了知识感知的上下文表示和上下文感知的知识表示，然后在此基础上通过迭代参考来收集深度匹配特征以评分响应候选者，实验结果表明 FIRE 在 PERSONA-CHAT 数据集和 CMU_DoG 数据集上的表现优于以前的方法，同时具有更好的可解释性。