  To improve deep-learning performance in low-resource settings, many
researchers have redesigned model architectures or applied additional data
(e.g., external resources, unlabeled samples). However, there have been
relatively few discussions on how to make good use of small amounts of labeled
samples, although it is potentially beneficial and should be done before
applying additional data or redesigning models. In this study, we assume a
low-resource setting in which only a few labeled samples (i.e., 30-100 per
class) are available, and we discuss how to exploit them without additional
data or model redesigns. We explore possible approaches in the following three
aspects: training-validation splitting, early stopping, and weight
initialization. Extensive experiments are conducted on six public sentence
classification datasets. Performance on various evaluation metrics (e.g.,
accuracy, loss, and calibration error) significantly varied depending on the
approaches that were combined in the three aspects. Based on the results, we
propose an integrated method, which is to initialize the model with a weight
averaging method and use a non-validation stop method to train all samples.
This simple integrated method consistently outperforms the competitive methods;
e.g., the average accuracy of six datasets of this method was 1.8% higher than
those of conventional validation-based methods. In addition, the integrated
method further improves the performance when adapted to several
state-of-the-art models that use additional data or redesign the network
architecture (e.g., self-training and enhanced structural models). Our results
highlight the importance of the training strategy and suggest that the
integrated method can be the first step in the low-resource setting. This study
provides empirical knowledge that will be helpful when dealing with
low-resource data in future efforts.
