Papers by Tianyuan Liu
End-to-end Adversarial Sample Generation for Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for generating adversarial samples have deceived many neural inference models, such as text classification and machine translation. |
| Approach: | They propose an adversarial sample generator that consists of a conditioned paraphrasing model and a condition generator and introduce a pretrained discriminator to help the adversarial sample generator adapt to the data characteristics. |
| Outcome: | The proposed approach improves the performance of the trained model on several tasks and is robust for various attacking techniques. |
BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks (2025.emnlp-main)
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Tianyuan Huang, Zepeng Zhu, Hangdi Xing, Zirui Shao, Zhi Yu, Chaoxiong Yang, Jiaxian He, Xiaozhong Liu, Jiajun Bu
| Challenge: | Existing Braille research focuses on isolated tasks while mixed-content Braille tasks face data scarcity and ambiguities. |
| Approach: | They propose a syntax tree-based augmentation method tailored for Braille data. |
| Outcome: | The proposed method improves Braille translation, formula-to-Braille conversion, and mixed-text translation. |
FuseGen: PLM Fusion for Data-generation based Zero-shot Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to train Small Task-specific Models (STMs) using synthetic datasets are limited by the low quality of such datasets. |
| Approach: | They propose a data-generation based zero-shot learning framework that uses multiple PLMs to train small task-specific models. |
| Outcome: | The proposed framework outperforms existing methods in boosting performance across tasks. |