Papers with learning high-quality semantic representations

1 papers
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding (2021.acl-long)

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Challenge: Pre-trained language models are vulnerable to simple perturbations, causing poor robustness . recent studies show that adversarial training is useless or harmful for the model to detect these semantic changes.
Approach: They propose to use adversarial training to improve the robustness of pre-trained models . they propose to construct negative examples with similar and opposite semantics .
Outcome: Empirical results show that the proposed approach improves on sentiment analysis, reasoning, and reading comprehension tasks.

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