For text classification tasks, finetuned language models perform remarkably
well. Yet, they tend to rely on spurious patterns in training data, thus
limiting their performance on out-of-distribution (OOD) test data. Among recent
models aiming to avoid this spurious pattern problem, adding extra
counterfactual samples to the training data has proven to be very effective.
Yet, counterfactual data generation is costly since it relies on human
annotation. Thus, we propose a novel solution that only requires annotation of
a small fraction (e.g., 1%) of the original training data, and uses automatic
generation of extra counterfactuals in an encoding vector space. We demonstrate
the effectiveness of our approach in sentiment classification, using IMDb data
for training and other sets for OOD tests (i.e., Amazon, SemEval and Yelp). We
achieve noticeable accuracy improvements by adding only 1% manual
counterfactuals: +3% compared to adding +100% in-distribution training samples,
+1.3% compared to alternate counterfactual approaches.

针对文本分类任务中的模型过度依赖模式数据的问题，本文提出了一种新的解决方案，使用编码向量空间的自动生成机制减少新数据生成的人工成本，只需对原始训练数据的一小部分（例如 1％）进行注释即可获得明显的准确性改进。实验证明，该方法对情感分类有效，并在使用 IMDb 数据进行训练和 Amazon、SemEval 和 Yelp 等其他数据集进行 OOD 测试时，仅添加 1％手动反事实数据即可获得 + 3％的准确性提升，相比于添加 100％的训练样本和其他反事实方法比较，准确性提高了 1.3％。