Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots (2020)

Abstract: 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.

## introduction building a conversational agent with intelligence has received significant attention with the emergence of personal assistants such as apple siri, google now and microsoft cortana. one approach is to building retrieval-based chatbots, which aims to select a potential response from a set of candidates given the conversation context @xcite @xcite @xcite . ## task definition given a dataset d, an example is represented as (c, k, r, y). specifically, @xmath0, u 2 , ..., u nc } represents a context with {u m } nc @xmath1. @xmath2, e 2 , ..., e n k } represents a knowledge description with {e n } n k @xmath3. r represents a response candidate. @xmath4, 1} denotes a label. @xmath5, k); otherwise, @xmath6. our goal is to learn a matching model g(c, k, r) from d. for any context-knowledge-response triple (c, k, r), g(c, k, r) measures the matching degree between (c, k) and r. ## fire model figure 2 shows the overview architecture of our proposed model. the context utterances, knowledge entries and responses are first encoded by a sentence encoder. then the context and the knowledge are co-filtered by referring to each other. next, the response refers to the filtered context and knowledge representations iteratively. the outputs of each iteration are aggregated into a matching feature vector, and are utilized as the inputs of next iteration at the same time. finally, the matching features of all iterations are accumulated for scoring response candidates. details are provided in following subsections. ## conclusion in this paper, we propose a method named filtering before iteratively referring (fire) for utilizing the background knowledge of dialogue agents