Web: http://arxiv.org/abs/2109.02431

May 5, 2022, 1:11 a.m. | Lan Jiang, Tianshu Lyu, Yankai Lin, Meng Chong, Xiaoyong Lyu, Dawei Yin

cs.CL updates on arXiv.org arxiv.org

Despite the remarkable success deep models have achieved in Textual Matching
(TM) tasks, it still remains unclear whether they truly understand language or
measure the semantic similarity of texts by exploiting statistical bias in
datasets. In this work, we provide a new perspective to study this issue -- via
the length divergence bias. We find the length divergence heuristic widely
exists in prevalent TM datasets, providing direct cues for prediction. To
determine whether TM models have adopted such heuristic, we …

arxiv bias divergence models on

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