1.2k citations · 1.3k across the 35 of their papers we have counts for
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Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Haochun Wang, Chi Liu, Nuwa Xi +7
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prom…
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
Tingting Wu, Xiao Ding, Hao Zhang +4
Given data with label noise (i.e., incorrect data), deep neural networks would gradually memorize the label noise and impair model performance. To relieve this issue, curriculum le…
Text Difficulty Study: Do machines behave the same as humans regarding text difficulty?
Bowen Chen, Xiao Ding, Li Du +2
Given a task, human learns from easy to hard, whereas the model learns randomly. Undeniably, difficulty insensitive learning leads to great success in NLP, but little attention has…
A Graph Enhanced BERT Model for Event Prediction
Li Du, Xiao Ding, Yue Zhang +3
Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previou…
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Li Du, Xiao Ding, Kai Xiong +2
Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can…