FAQ-Based Question Answering via Knowledge Anchors
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 Knowl-edge Anchor based Question Answering(KAQA)framework for FAQ-based QA to better understand questions and retrieve more appropri-ate 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 knowl-edge anchors to precisely capture the core semantics,which brings in higher precision and better interpretability.The multi-channel match-ing strategy also enables most sentence matching models to be flexibly plugged in our KAQA framework to fit different real-world computa-tion 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.
Ruobing Xie Yanan Lu Fen Lin Leyu Lin
WeChat Search Application Department,Tencent,Beijing,China
国际会议
9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)
郑州
英文
3-15
2020-10-14（万方平台首次上网日期，不代表论文的发表时间）