Papers by Xinchen Ma

2 papers
Unsupervised Text Style Transfer for Controllable Intensity (2026.findings-eacl)

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Challenge: Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs.
Approach: They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels.
Outcome: The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity.
Diff4TST: Masked Diffusion Language Model for Text Style Transfer (2026.acl-long)

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Challenge: Existing methods for text style transfer rely on task-specific training and expensive training stages.
Approach: They propose a diffusion-based language model that formulates text style transfer as an explicit copy-and-edit process.
Outcome: The proposed model improves style accuracy and controllability while maintaining strong content preservation and fluency.

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