Papers by Jin Qu
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning (2024.findings-eacl)
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| Challenge: | Cross-lingual transfer of language models trained on high-resource languages such as English has been limited due to the high cost of obtaining non-English conversational data. |
| Approach: | They introduce a parallel and large-scale multilingual conversation dataset that is used for cross-lingual alignment pretraining by translating the English-only Schema-Guided Dialogue dataset into 105 other languages. |
| Outcome: | The proposed model performs well on slot-filling and intent classification tasks, and is able to perform well in other languages. |
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
BatchMixup: Improving Training by Interpolating Hidden States of the Entire Mini-batch (2021.findings-acl)
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| Challenge: | a data augmentation technique is used to augment data, but it has two drawbacks. |
| Approach: | They propose a new mixup paradigm that generates new points scattered throughout the whole mini-batch. |
| Outcome: | The proposed model improves the performance of NLP tasks while using different ratios of training data. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? (2022.findings-acl)
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| Challenge: | Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer. |
| Approach: | They propose to transfer semantic knowledge from rich-resourced languages to low-resource languages by using multilingual transfer learning. |
| Outcome: | The proposed model outperforms baselines and can handle negative transfer. |
XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics (2026.findings-acl)
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| Challenge: | averaging metric scores across languages is suspicious since translations of equal quality receive different scores across language. |
| Approach: | They propose a semi-automatically built dataset to benchmark translation metrics using MQM-defined errors and a normalization strategy to mitigate cross-lingual scoring bias. |
| Outcome: | The proposed model shows that translation metrics suffer from cross-lingual scoring bias . the proposed model is based on a semi-automatically built dataset covering nine translation directions . |
Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs (2020.emnlp-main)
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| Challenge: | Existing methods for reasoning over temporal knowledge graphs focus on past timestamps and are not able to predict future interactions. |
| Approach: | They propose a novel autoregressive architecture for predicting future interactions using a recurrent event encoder and a neighborhood aggregator. |
| Outcome: | The proposed method achieves state-of-the-art on five public datasets. |
Collaborative Policy Learning for Open Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing models of knowledge graph reasoning suffer from limited performance when working on sparse and incomplete graphs due to the lack of evidential paths that can reach target entities. |
| Approach: | They propose a framework to train two collaborative agents to reason for missing facts over a graph augmented by a text corpus. |
| Outcome: | Experiments on two public datasets show the proposed approach is effective on a knowledge graph reasoning task. |
TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce (2023.emnlp-industry)
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| Challenge: | Annually, e-commerce platforms incur substantial financial losses due to trademark infringements. |
| Approach: | They propose a dataset to detect trademark infringement in merchant registrations . they use legal rules and contextual information from Alipay to gather contextual information with annotations from legal experts. |
| Outcome: | The proposed dataset is sourced from Alipay, one of the world’s largest e-commerce and digital payment platforms. |
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding (2024.emnlp-main)
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| Challenge: | Large language models have shown a powerful ability for text generation, but undesired behaviors such as toxicity and hallucinations can manifest. |
| Approach: | They propose to formalize text generation as a future-constrained generation problem to minimize undesirable behaviors and enforce faithfulness to instructions. |
| Outcome: | The proposed approach is effective across three tasks, including keyword-constrained generation, toxicity reduction, and factual correctness in question-answering. |