Papers with transfer fine-tuning

2 papers
Paraphrase-based Contrastive Learning for Sentence Pair Modeling (2025.naacl-srw)

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Challenge: Existing methods to improve performance of sentence pair modeling are not available on a large-scale for non-English languages.
Approach: They propose a method to apply contrastive learning to pre-trained masked language models . they use sentence embeddings of paraphrase pairs to make similar sentences .
Outcome: The proposed method can be used on four sentence pair modeling tasks in English and Japanese.
Transfer Fine-tuning for Quality Estimation of Text Simplification (2024.lrec-main)

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Challenge: Experimental results show that quality estimation of text simplification models can be improved on a small labeled corpus.
Approach: They propose a method to train quality estimation of text simplification on a small-scale labeled corpus prior to fine-tuning pre-trained language models.
Outcome: The proposed method improves quality estimation of text simplification on a small-scale labeled corpus.

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