  Scientific Natural Language Inference (NLI) is the task of predicting the
semantic relation between a pair of sentences extracted from research articles.
The automatic annotation method based on distant supervision for the training
set of SciNLI (Sadat and Caragea, 2022b), the first and most popular dataset
for this task, results in label noise which inevitably degenerates the
performance of classifiers. In this paper, we propose a novel co-training
method that assigns weights based on the training dynamics of the classifiers
to the distantly supervised labels, reflective of the manner they are used in
the subsequent training epochs. That is, unlike the existing semi-supervised
learning (SSL) approaches, we consider the historical behavior of the
classifiers to evaluate the quality of the automatically annotated labels.
Furthermore, by assigning importance weights instead of filtering out examples
based on an arbitrary threshold on the predicted confidence, we maximize the
usage of automatically labeled data, while ensuring that the noisy labels have
a minimal impact on model training. The proposed method obtains an improvement
of 1.5% in Macro F1 over the distant supervision baseline, and substantial
improvements over several other strong SSL baselines. We make our code and data
available on Github.
