NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering
Minhao Zhang, Ruoyu Zhang, Lei Zou, Yinnian Lin, Sen Hu
Abstract
We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. Equipped with techniques including data augmentation and multitasking, we show that the proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. Moreover, we develop a novel data annotation strategy that facilitates the node-to-mention alignment, a dataset (https://github.com/ridiculouz/CKBQA) with such strategy is also published to promote further research. An online demo of NAMER (http://kbqademo.gstore.cn) is provided to visualize our framework and supply extra information for users, a video illustration (https://youtu.be/yetnVye_hg4) of NAMER is also available.
BibTeX
@inproceedings{zhang-etal-2021-namer,
title = "{NAMER}: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering",
author = "Zhang, Minhao and
Zhang, Ruoyu and
Zou, Lei and
Lin, Yinnian and
Hu, Sen",
editor = "Sil, Avi and
Lin, Xi Victoria",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-demos.3/",
doi = "10.18653/v1/2021.naacl-demos.3",
pages = "18--25"
}