Selection Bias Explorations and Debias Methods for
Natural Language Sentence Matching Datasets
Abstract
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 na¨?ve 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