用数据增强提升小模型对否定句的判断能力
Analyzing and Mitigating Negation Artifacts using Data Augmentation for Improving ELECTRA-Small Model Accuracy
- 通过构建反例和对抗样本增强训练数据
- 否定相关样本准确率显著提升，整体性能不变
- 适合关注模型鲁棒性与语义理解的研究者
用于自然语言推理的预训练模型常依赖数据集中的虚假关联而非真正理解语言，如否定表达。本研究分析在斯坦福自然语言推理（SNLI）数据集上微调的ELECTRA-small模型对否定句的处理能力，发现其在包含否定的样本上表现不佳。为此，我们通过引入对比集和强调否定的对抗样本来扩充训练数据。结果表明，这种针对性的数据增强能有效提升模型在否定相关样本上的准确率，且不影响整体性能，从而缓解了该数据集中的特定伪相关问题。
原文摘要 · Abstract (English)
Pre-trained models for natural language inference (NLI) often achieve high performance on benchmark datasets by using spurious correlations, or dataset artifacts, rather than understanding language touches such as negation. In this project, we investigate the performance of an ELECTRA-small model fine-tuned on the Stanford Natural Language Inference (SNLI) dataset, focusing on its handling of negation. Through analysis, we identify that the model struggles with correctly classifying examples containing negation. To address this, we augment the training data with contrast sets and adversarial examples emphasizing negation. Our results demonstrate that this targeted data augmentation improves the model's accuracy on negation-containing examples without adversely affecting overall performance, therefore mitigating the identified dataset artifact.
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