期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：3428-3439
DOI：10.18653/v1/2021.eacl-main.300
语种：English
出版社：ACL Anthology
摘要：Question answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG. In a real-world industrial setting, this involves addressing multiple challenges including entity linking, multi-hop reasoning over KG, etc. Traditional approaches handle these challenges in a modularized sequential manner where errors in one module lead to the accumulation of errors in downstream modules. Often these challenges are inter-related and the solutions to them can reinforce each other when handled simultaneously in an end-to-end learning setup. To this end, we propose a multi-task BERT based Neural Machine Translation (NMT) model to address these challenges. Through experimental analysis, we demonstrate the efficacy of our proposed approach on one publicly available and one proprietary dataset.
