# Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots

arXiv: https://arxiv.org/abs/2004.14550 · HTML: https://mlsanity.com/paper/2004.14550/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Retrieving Multimodal Information for Augmented Generation: A Survey](https://mlsanity.com/paper/2303.10868/) (2023)
- [Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2105.09050/) (2021)
- [Ask LLMs Directly, "What shapes your bias?": Measuring Social Bias in Large Language Models](https://mlsanity.com/paper/2406.04064/) (2024)
- [Detecting Speaker Personas from Conversational Texts](https://mlsanity.com/paper/2109.01330/) (2021)
- [P5: Plug-and-Play Persona Prompting for Personalized Response Selection](https://mlsanity.com/paper/2310.06390/) (2023)
- [Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access](https://mlsanity.com/paper/2212.05373/) (2022)
- [Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection](https://mlsanity.com/paper/2208.09601/) (2022)
- [Task Oriented Conversational Modelling With Subjective Knowledge](https://mlsanity.com/paper/2303.17695/) (2023)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [Multi-turn Response Selection with Commonsense-enhanced Language Models](https://mlsanity.com/paper/2407.18479/) (2024)

## Abstract (arXiv)

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.