通过数据增强与预处理提升NLI模型在困难数据上的鲁棒性
Improving the Natural Language Inference robustness to hard dataset by data augmentation and preprocessing
- 针对词重叠、数值推理和长度不匹配问题设计通用增强方法
- 在Hard NLI数据集上显著提升模型准确率，缓解分布外性能下降
- 适合需要提升模型泛化能力的研究者与实际应用开发者
自然语言推理（NLI）任务旨在判断假设是否可由前提推断得出，通常将假设分为蕴含、中立和矛盾三类。尽管基于Transformer的模型在常规数据集上表现优异，但在面对困难数据时仍表现不佳，尤其在未见过的分布外前提与假设上，模型可能依赖表面关联而非语义理解。本文提出数据增强与预处理方法，有效解决词重叠、数值推理和长度不匹配等问题。这些方法不依赖测试数据分布，可通用提升模型鲁棒性，在多个Hard NLI数据集上验证了有效性。
原文摘要 · Abstract (English)
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.
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