Natural Language Sentence Matching (NLSM) has gained substantial attention
from both academics and the industry, and rich public datasets contribute a lot
to this process. However, biased datasets can also hurt the generalization
performance of trained models and give untrustworthy evaluation results. For
many NLSM datasets, the providers select some pairs of sentences into the
datasets, and this sampling procedure can easily bring unintended pattern,
i.e., selection bias. One example is the QuoraQP dataset, where some
content-independent naive features are unreasonably predictive. Such features
are the reflection of the selection bias and termed as the leakage features. In
this paper, we investigate the problem of selection bias on six NLSM datasets
and find that four out of them are significantly biased. We further propose a
training and evaluation framework to alleviate the bias. Experimental results
on QuoraQP suggest that the proposed framework can improve the generalization
ability of trained models, and give more trustworthy evaluation results for
real-world adoptions.

本文对自然语言句子匹配（NLSM）中可能出现的选择偏见进行了调查，并提出了一种训练和评估框架以减轻偏见带来的负面影响，实验结果表明该框架可以提高训练模型的泛化能力，并给出更加可信的评估结果。