Papers by Xinrui Hu
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Seeing the Same Story Differently: Framing‐Divergent Event Coreference for Computational Framing Analysis (2025.emnlp-main)
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| Challenge: | a new task aims to capture subtle differences in how news articles frame events . a central challenge is capturing how same real-world event can evolve into sharply divergent narratives . |
| Approach: | They propose a task that identifies pairs of event mentions referring to the same underlying occurrence but differing in framing across documents. |
| Outcome: | The proposed method enables scalable, interpretable analysis of how media frame the same events differently. |
Beyond Benchmarks: Building a Richer Cross-Document Event Coreference Dataset with Decontextualization (2025.naacl-long)
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| Challenge: | Existing datasets for Cross-Document Event Coreference (CDEC) are small and lacking diversity. |
| Approach: | They propose a new approach leveraging large language models to decontextualize event mentions by simplifying the document-level annotation task to sentence pairs with enriched context. |
| Outcome: | The proposed approach improves the quality of the dataset and generalizability of the model. |
Reframing Responsibility: Framing-Aware Event Causality Identification (2026.acl-long)
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| Challenge: | Causal explanations in political narratives are often framed and contested. |
| Approach: | They propose a framing-aware extension of ECI that models causal explanations as structured claims including responsibility targets, evaluative frams, source type, and epistemic modality. |
| Outcome: | The proposed model enables quantitative analysis of divergent causal attribution across narratives. |