GLS-CSC: A Simple but Effective Strategy to Mitigate Chinese STM Models'
  Over-Reliance on Superficial Clue
Yanrui Du Sendong Zhao Yuhan Chen Rai Bai Jing Liu Hua Wu Haifeng Wang Bing Qin
Pre-trained models have achieved success in Chinese Short Text Matching (STM)
tasks, but they often rely on superficial clues, leading to a lack of robust
predictions. To address this issue, it is crucial to analyze and mitigate the
influence of superficial clues on STM models. Our study aims to investigate
their over-reliance on the edit distance feature, commonly used to measure the
semantic similarity of Chinese text pairs, which can be considered a
superficial clue. To mitigate STM models' over-reliance on superficial clues,
we propose a novel resampling training strategy called Gradually Learn Samples
Containing Superficial Clue (GLS-CSC). Through comprehensive evaluations of
In-Domain (I.D.), Robustness (Rob.), and Out-Of-Domain (O.O.D.) test sets, we
demonstrate that GLS-CSC outperforms existing methods in terms of enhancing the
robustness and generalization of Chinese STM models. Moreover, we conduct a
detailed analysis of existing methods and reveal their commonality.