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arXiv · 1909.08905

A Split-and-Recombine Approach for Follow-up Query Analysis

Qian Liu·Bei Chen·Haoyan Liu·Lei Fang·Jian-Guang Lou·Bin Zhou·Dongmei Zhang
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Abstract

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.

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Qian Liu, Bei Chen, Haoyan Liu, Lei Fang, Jian-Guang Lou, Bin Zhou, Dongmei Zhang. 2019-09-19. A Split-and-Recombine Approach for Follow-up Query Analysis. https://arxiv.org/abs/1909.08905

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