  For high-resource languages like English, text classification is a
well-studied task. The performance of modern NLP models easily achieves an
accuracy of more than 90% in many standard datasets for text classification in
English (Xie et al., 2019; Yang et al., 2019; Zaheer et al., 2020). However,
text classification in low-resource languages is still challenging due to the
lack of annotated data. Although methods like weak supervision and
crowdsourcing can help ease the annotation bottleneck, the annotations obtained
by these methods contain label noise. Models trained with label noise may not
generalize well. To this end, a variety of noise-handling techniques have been
proposed to alleviate the negative impact caused by the errors in the
annotations (for extensive surveys see (Hedderich et al., 2021; Algan & Ulusoy,
2021)). In this work, we experiment with a group of standard noisy-handling
methods on text classification tasks with noisy labels. We study both simulated
noise and realistic noise induced by weak supervision. Moreover, we find
task-adaptive pre-training techniques (Gururangan et al., 2020) are beneficial
for learning with noisy labels.
