Improving the Natural Language Inference robustness to hard dataset by data augmentation and preprocessing
This work addresses robustness issues in NLI models for AI and NLP applications, but it is incremental as it builds on existing methods.
The paper tackles the problem of Natural Language Inference models performing poorly on hard datasets, especially with out-of-distribution data, by proposing data augmentation and preprocessing methods to address issues like word overlap, numerical reasoning, and length mismatch, resulting in improved model robustness.
Natural Language Inference (NLI) is the task of inferring whether the hypothesis can be justified by the given premise. Basically, we classify the hypothesis into three labels(entailment, neutrality and contradiction) given the premise. NLI was well studied by the previous researchers. A number of models, especially the transformer based ones, have achieved significant improvement on these tasks. However, it is reported that these models are suffering when they are dealing with hard datasets. Particularly, they perform much worse when dealing with unseen out-of-distribution premise and hypothesis. They may not understand the semantic content but learn the spurious correlations. In this work, we propose the data augmentation and preprocessing methods to solve the word overlap, numerical reasoning and length mismatch problems. These methods are general methods that do not rely on the distribution of the testing data and they help improve the robustness of the models.