Papers by Haicheng Wang
Neural Search Space in Gboard Decoder (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Gboard decoder uses context, a lexicon and language models to provide a user-friendly keyboard. |
| Approach: | They propose a Neural Search Space which replaces an N-gram LM with a neural network LM and dynamically constructs the search space during decoding. |
| Outcome: | The proposed system improves the quality of the decoded keyboards on various locales with acceptable latency increases. |
Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings (2025.findings-acl)
Copied to clipboard
Shujian Yang, Shiyao Cui, Chuanrui Hu, Haicheng Wang, Tianwei Zhang, Minlie Huang, Jialiang Lu, Han Qiu
| Challenge: | Recent studies show that character substitutions in toxic Chinese text can confuse state-of-the-art LLMs. |
| Approach: | They propose a taxonomy of 3 perturbation strategies and 8 specific approaches in Chinese text to assess if they can detect perturbed Chinese toxic contents. |
| Outcome: | The proposed model can detect perturbed Chinese text with 8 different approaches . the proposed model is compared with 9 other LLMs from the US and China . |
TOXIFRENCH: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection (2026.findings-acl)
Copied to clipboard
| Challenge: | toxicity detection in French remains underdeveloped due to the lack of culturally relevant, human-annotated, large-scale datasets. |
| Approach: | They propose a method that generalizes French online comments using a semi-automated annotation pipeline that reduces manual labeling to only 10% through high-confidence LLM-based pre-annotation and human verification. |
| Outcome: | The proposed model outperforms GPT-4o and DeepSeek-R1 on the benchmark while maintaining cross-lingual capabilities. |
POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion (2025.emnlp-main)
Copied to clipboard
Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Zhou Xiao, Yang Yu, Jie Zhou
| Challenge: | Existing approaches to training document conversion models with manual annotation are costly and time-consuming, and training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications. |
| Approach: | They propose a fully automated framework for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts. |
| Outcome: | The proposed model outperforms existing models and improves on annotated documents. |