Papers with semantic bias
Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models (2021.acl-short)
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| Challenge: | Pretrained language models (PLMs) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. |
| Approach: | They propose to add a regularization loss to the existing COPA models to mitigate the problem of semantic similarity bias by adding a normalization loss. |
| Outcome: | The proposed model improves generalization ability and performs better on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect. |
Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)
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| Challenge: | Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations. |
| Approach: | They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets. |