The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on the background knowledge and how to perform their matching with the response . This paper proposes a method named Filtering before Iteratively REferring (FIRE) for presenting the background knowledge of dialogue agents in retrieval-based chatbots. We first propose a pre-filter, which is composed of a context filter and a knowledge filter . This pre-filter grounds the conversation on the knowledge and comprehends the knowledge according to the conversation by collecting the matching information between them bidirectionally, and then recognizing the important information in them accordingly . After that, iteratively referring is performed between the context and the response , as well as between the knowledge and the response , in order to collect the deep and wide matching information . Experimental results show that the FIRE model outperforms previous methods by margins larger than 2.8\% on original personas and 4.1\% on revised personas on the PERSONA-CHAT dataset , as well as 3.1\% on the CMU_DoG dataset in terms of top-1 accuracy  .
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 .
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