@inproceedings{chen2021retrack,
author = {Chen, Shuang and Liu, Qian and Yu, Zhiwei and Lin, Chin-Yew and Lou, Jian-Guang and Jiang, Feng},
title = {ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering},
booktitle = {ACL-IJCNLP 2021 (Demo Track)},
year = {2021},
month = {August},
abstract = {We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.},
publisher = {Association for Computational Linguistics (ACL)},
url = {http://approjects.co.za/?big=en-us/research/publication/retrack-a-flexible-and-efficient-framework-for-knowledge-base-question-answering/},
pages = {325-336},
}