Papers by Xuebin Wang
Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation (2025.coling-main)
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| Challenge: | Recent studies on Chinese Word Segmentation (CWS) have focused on the cross-domain scenarios, but there is a high cost of manually annotating high-quality data. |
| Approach: | They propose to explicitly mine word boundaries from parallel speech-text data by using the Montreal Forced Aligner toolkit to perform character-level alignment on speech- text data. |
| Outcome: | The proposed approach is based on character-level alignment on speech-text data and a robust complete-then-train (CTT) strategy. |
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (2020.findings-emnlp)
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| Challenge: | Event detection (ED) is a key subtask of information extraction. |
| Approach: | They propose an architecture that exploits syntactic structure and typed dependency label information to perform event detection. |
| Outcome: | The proposed architecture exploits syntactic structure and typed dependency label information to perform ED. |
Cognitive Analysis Graph-Guided Multi-Turn Safety Enhancement for Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods to align large language models with human preferences lack relationship consideration between question and response. |
| Approach: | They propose an inference-time safety alignment framework that explicitly models the cognitive process of problem solving through a structured cognitive analysis graph. |
| Outcome: | The proposed framework enables transparent, controllable reasoning while maintaining strong safety guarantees. |
A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese (2025.coling-demos)
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| Challenge: | Existing tools for word segmentation are based on different linguistic theories or target different scenarios. |
| Approach: | They propose a probabilistic toolkit for multi-grained word segmentation in Chinese . they adopt semi-Markov CRF for single-grain word segmenting (SWS) . |
| Outcome: | The proposed approach can produce marginal probabilities of words during inference and significantly improve performance in the cross-domain scenario. |