Ya Guo


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Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
Chong Zhang | Ya Guo | Yi Tu | Huan Chen | Jinyang Tang | Huijia Zhu | Qi Zhang | Tao Gui
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs), in which named entity recognition (NER) is treated as a sequence-labeling task of predicting the BIO entity tags for tokens, following the typical setting of NLP. However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems. Such reading order issue hinders the accurate marking of entities by BIO-tagging scheme, making it impossible for sequence-labeling methods to predict correct named entities. To address the reading order issue, we introduce Token Path Prediction (TPP), a simple prediction head to predict entity mentions as token sequences within documents. Alternative to token classification, TPP models the document layout as a complete directed graph of tokens, and predicts token paths within the graph as entities. For better evaluation of VrD-NER systems, we also propose two revised benchmark datasets of NER on scanned documents which can reflect real-world scenarios. Experiment results demonstrate the effectiveness of our method, and suggest its potential to be a universal solution to various information extraction tasks on documents.

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LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding
Yi Tu | Ya Guo | Huan Chen | Jinyang Tang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Visually-rich Document Understanding (VrDU) has attracted much research attention over the past years. Pre-trained models on a large number of document images with transformer-based backbones have led to significant performance gains in this field. The major challenge is how to fusion the different modalities (text, layout, and image) of the documents in a unified model with different pre-training tasks. This paper focuses on improving text-layout interactions and proposes a novel multi-modal pre-training model, LayoutMask. LayoutMask uses local 1D position, instead of global 1D position, as layout input and has two pre-training objectives: (1) Masked Language Modeling: predicting masked tokens with two novel masking strategies; (2) Masked Position Modeling: predicting masked 2D positions to improve layout representation learning. LayoutMask can enhance the interactions between text and layout modalities in a unified model and produce adaptive and robust multi-modal representations for downstream tasks. Experimental results show that our proposed method can achieve state-of-the-art results on a wide variety of VrDU problems, including form understanding, receipt understanding, and document image classification.


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Generating Abbreviations for Chinese Named Entities Using Recurrent Neural Network with Dynamic Dictionary
Qi Zhang | Jin Qian | Ya Guo | Yaqian Zhou | Xuanjing Huang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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A Generative Model for Identifying Target Companies of Microblogs
Yeyun Gong | Yaqian Zhou | Ya Guo | Qi Zhang | Xuanjing Huang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers