Hybrid mutation driven testing for natural language inference

Abstract

Natural language inference (NLI) is a task to infer the relationship between the premise and hypothesis sentences, whose models have essential applications in the many natural language processing (NLP) fields, for example, machine reading comprehension and recognizing textual entailment. Due to the data-driven programming paradigm, bugs inevitably occur in NLI models during the application process, which calls for novel automatic testing techniques to deal with NLI testing challenges. The main difficulty in achieving automatic testing for NLI models is the oracle problem; that is, it may be too expensive to label NLI model inputs manually and hence be too challenging to verify the correctness of model outputs. To tackle the oracle problem, this study proposes a novel automatic testing method hybrid mutation driven testing (HMT), which extends the mutation idea applied in other NLP domains successfully. Specifically, as there are two sets of sentences, that is, premise and hypothesis, to be mutated, we propose four mutation operators to achieve the hybrid mutation strategy, which mutate the premise and the hypothesis sentences jointly or individually. We assume that the mutation would not affect the outputs; that is, if the original and mutated outputs are inconsistent, inconsistency bugs could be detected without knowing the true labels. To evaluate our method HMT, we conduct experiments on two widely used datasets with two advanced models and generate more than 520,000 mutations by applying our mutation operators. Our experimental results show that (a) our method, HMT, can effectively generate mutated testing samples, (b) our method can effectively trigger the inconsistency bugs of the NLI models, and (c) all four mutation operators can independently trigger inconsistency bugs.