1 research outputs found
FAQ-based Question Answering via Knowledge Anchors
Question answering (QA) aims to understand user 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) utilized 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 parts:
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 enable most sentence
matching models to be flexibly plugged in out KAQA framework to fit different
real-world computation costs. In experiments, we evaluate our models on a
query-document matching task over a real-world FAQ-based QA dataset, with
detailed analysis over different settings and cases. The results confirm the
effectiveness and robustness of the KAQA framework in real-world FAQ-based QA.Comment: 9 page