Wenhui Liao


2024

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PPTSER: A Plug-and-Play Tag-guided Method for Few-shot Semantic Entity Recognition on Visually-rich Documents
Wenhui Liao | Jiapeng Wang | Zening Lin | Longfei Xiong | Lianwen Jin
Findings of the Association for Computational Linguistics ACL 2024

Visually-rich document information extraction (VIE) is a vital aspect of document understanding, wherein Semantic Entity Recognition (SER) plays a significant role. However, few-shot SER on visually-rich documents remains relatively unexplored despite its considerable potential for practical applications. To address this issue, we propose a simple yet effective Plug-and-Play Tag-guided method for few-shot Semantic Entity Recognition (PPTSER) on visually-rich documents. PPTSER is built upon off-the-shelf multi-modal pre-trained models. It leverages the semantics of the tags to guide the SER task, reformulating SER into entity typing and span detection, handling both tasks simultaneously via cross-attention. Experimental results illustrate that PPTSER outperforms existing fine-tuning and few-shot methods, especially in low-data regimes. With full training data, PPTSER achieves comparable or superior performance to fine-tuning baseline. For instance, on the FUNSD benchmark, our method improves the performance of LayoutLMv3-base in 1-shot, 3-shot and 5-shot scenarios by 15.61%, 2.13%, and 2.01%, respectively. Overall, PPTSER demonstrates promising generalizability, effectiveness, and plug-and-play nature for few-shot SER on visually-rich documents. The codes will be available at [https://github.com/whlscut/PPTSER](https://github.com/whlscut/PPTSER).

2009

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Integrating High Precision Rules with Statistical Sequence Classifiers for Accuracy and Speed
Wenhui Liao | Marc Light | Sriharsha Veeramachaneni
Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing (SETQA-NLP 2009)

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A Simple Semi-supervised Algorithm For Named Entity Recognition
Wenhui Liao | Sriharsha Veeramachaneni
Proceedings of the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing