FAQ-Based Question Answering via Knowledge Anchors

Abstract

Question answering (QA) aims to understand questions and find appropriate answers. In real-world QA systems, Frequently Asked Question (FAQ) based QA is usually a practical and effective solution, especially for some complicated questions (e.g., How and Why). Recent years have witnessed the great successes of knowledge graphs (KGs) in KBQA systems, while there are still few works focusing on making full use of KGs in FAQ-based QA. In this paper, we propose a novel Knowledge Anchor based Question Answering (KAQA) framework for FAQ-based QA to better understand questions and retrieve more appropriate answers. More specifically, KAQA mainly consists of three modules: knowledge graph construction, query anchoring and query-document matching. We consider entities and triples of KGs in texts as knowledge anchors to precisely capture the core semantics, which brings in higher precision and better interpretability. The multi-channel matching strategy also enables most sentence matching models to be flexibly plugged in our KAQA framework to fit different real-world computation limitations. In experiments, we evaluate our models on both offline and online query-document matching tasks on a real-world FAQ-based QA system in WeChat Search, with detailed analysis, ablation tests and case studies. The significant improvements confirm the effectiveness and robustness of the KAQA framework in real-world FAQ-based QA.