Papers by Jingshen Zhang
Label Confidence Weighted Learning for Target-level Sentence Simplification (2024.emnlp-main)
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| Challenge: | Existing methods for sentence simplification use label confidence weighting to generate pseudo-labeled sentences with varying proficiency levels. |
| Approach: | They propose a label confidence weighting scheme for multi-level sentence simplification that incorporates a weighting system into the training loss of the encoder-decoder model. |
| Outcome: | The proposed approach outperforms state-of-the-art confidence weighting methods on English grade-level simplification datasets. |
Let’s Simplify Step by Step: Guiding LLM Towards Multilingual Unsupervised Proficiency-Controlled Sentence Simplification (2026.findings-eacl)
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| Challenge: | Large language models demonstrate limited capability in proficiency-controlled sentence simplification when simplifying across large readability levels. |
| Approach: | They propose a framework that decomposes complex simplifications into manageable steps through dynamic path planning, semantic-aware exemplar selection, and chain-of-thought generation with conversation history for coherent reasoning. |
| Outcome: | The proposed framework reduces computational steps while improving simplification effectiveness on five languages across two benchmarks. |