%0 Conference Proceedings
%T A Split-and-Recombine Approach for Follow-up Query Analysis
%A Liu, Qian
%A Chen, Bei
%A Liu, Haoyan
%A Lou, Jian-Guang
%A Fang, Lei
%A Zhou, Bin
%A Zhang, Dongmei
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F liu-etal-2019-split
%X Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8%. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising.
%R 10.18653/v1/D19-1535
%U https://aclanthology.org/D19-1535
%U https://doi.org/10.18653/v1/D19-1535
%P 5316-5326