As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

Despite the super-human accuracy of recent deep models in NLP tasks, their robustness is
reportedly limited due to their reliance on spurious patterns. We thus aim to leverage …

Recent studies have shown that strong Natural Language Understanding (NLU) models are
prone to relying on annotation biases of the datasets as a shortcut, which goes against the …

In text classification models, while the unsupervised attention mechanism can enhance
performance, it often produces attention distributions that are puzzling to humans, such as …

Recent studies show Pre-trained Language Models (PLMs) tend to shortcut learning,
reducing effectiveness with Out-Of-Distribution (OOD) samples, prompting research on the …

AI has introduced a new reform direction for traditional education, such as automating
Grammatical Error Correction (GEC) to reduce teachers' workload and improve efficiency …

In text classification, the traditional attention mechanisms usually focus too much on frequent
words, and need extensive labeled data in order to learn. This article proposes a …

Increasing attention has been drawn to the sentiment analysis of financial documents. The
most popular examples of such documents include analyst reports and economic news, the …