Papers by Xiaojing Zhao
Graph Convolution for Multimodal Information Extraction from Visually Rich Documents (N19-2)
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| Challenge: | Visually rich documents (VRDs) present information in the form of both text and vision. |
| Approach: | They propose a graph convolution based model to combine textual and visual information presented in VRDs. |
| Outcome: | The proposed model outperforms existing models on two real-world datasets. |
RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation (2025.findings-emnlp)
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Tianjiao Li, Mengran Yu, Chenyu Shi, Yanjun Zhao, Xiaojing Liu, Qi Zhang, Xuanjing Huang, Qiang Zhang, Jiayin Wang
| Challenge: | Using reinforcement learning from human feedback, large language models perform poorly when applied to colloquial subtitle translation tasks. |
| Approach: | They propose an adversarial training framework that iteratively updates the offline reward model and the online LLM to improve training outcomes. |
| Outcome: | The proposed training framework significantly improves upon translation baselines. |
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training (2024.lrec-main)
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| Challenge: | Experimental evaluations conducted on the CMeEE, a benchmark for Chinese nested medical named entity recognition (NER) model outperforms the compared state-of-the-art (SOTA) models. |
| Approach: | They propose a model based on machine reading comprehension that uses a task-adaptive pre-training strategy to improve the model’s capability in the medical field. |
| Outcome: | The proposed model outperforms the compared state-of-the-art models on the CMeEE, a benchmark for Chinese nested medical NER. |
Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention (2025.acl-long)
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| Challenge: | Autoregressive Large Language Models (LLMs) demonstrate exceptional performance in language understanding and generation tasks, but their application in text embedding tasks has been relatively slow due to the constraints of the unidirectional attention mechanism. |
| Approach: | They propose to enable bidirectional attention in LLMs to improve their ability to represent subsequent context but weaken their utilization of preceding context. |
| Outcome: | The proposed model improves the LLMs’ ability to represent subsequent context but weakens their utilization of preceding context, while contrastive learning training can help to maintain both abilities. |