Validation on machine reading comprehension software without annotated labels: a property-based method
Songqiang Chen, Shuo Jin, Xiaoyuan Xie
Abstract
Machine Reading Comprehension (MRC) in Natural Language Processing has seen great progress recently. But almost all the current MRC software is validated with a reference-based method, which requires well-annotated labels for test cases and tests the software by checking the consistency between the labels and the outputs. However, labeling test cases of MRC could be very costly due to their complexity, which makes reference-based validation hard to be extensible and sufficient. Furthermore, solely checking the consistency and measuring the overall score may not be sensible and flexible for assessing the language understanding capability. In this paper, we propose a property-based validation method for MRC software with Metamorphic Testing to supplement the reference-based validation. It does not refer to the labels and hence can make much data available for testing. Besides, it validates MRC software against various linguistic properties to give a specific and in-depth picture on linguistic capabilities of MRC software. Comprehensive experimental results show that our method can successfully reveal violations to the target linguistic properties without the labels. Moreover, it can reveal problems that have been concealed by the traditional validation. Comparison according to the properties provides deeper and more concrete ideas about different language understanding capabilities of the MRC software.