Generating Regular Expressions from Natural Language Specifications: Are We There Yet?
Zexuan Zhong, Jiaqi Guo, Wei Yang, Tao Xie, Jian-Guang Lou, Ting Liu, Dongmei Zhang. NLSE 2018
Recent state-of-the-art approaches automatically generate regular expressions from natural language specifications. Given that these approaches use only synthetic data in both training datasets and validation/test datasets, a natural question arises: are these approaches effective to address various real-world situations? To explore this question, in this paper, we conduct a characteristic study on comparing two synthetic datasets used by the recent research and a real-world dataset collected from the Internet, and conduct an experimental study on applying a state-of-the-art approach on the real-world dataset. Our study results suggest the existence of distinct characteristics between the synthetic datasets and the real-world dataset, and the state-of-the-art approach (based on a model trained from a synthetic dataset) achieves extremely low effectiveness when evaluated on real-world data, much lower than the effectiveness when evaluated on the synthetic dataset. We also provide initial analysis on some of those challenging cases and discuss future directions.
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