Papers by Yangfan Zhou

4 papers
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)

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Challenge: Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text.
Approach: They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types.
Outcome: The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages.
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)

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Challenge: Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning.
Approach: They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis.
Outcome: The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection.
COAS2W: A Chinese Older-Adults Spoken-to-Written Transformation Corpus with Context Awareness (2025.emnlp-main)

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Challenge: Existing work on spoken-to-written transformations from older adults' language is limited by omission, disordered syntax, constituent errors, and redundancy.
Approach: They propose to combine a spoken-to-written corpus of 10,004 utterances from older adults with a written version, fine-grained error labels, and four-sentence context.
Outcome: The proposed model outperforms closed-source models on Chinese spoken-to-written corpus and shows that multi-sentence input is more efficient.
Event-Centric Query Expansion in Web Search (2023.acl-industry)

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Challenge: Existing studies rely on long-term search log mining to improve search experience . EQE system is a novel event retrieval framework that can select the best expansion from a significant amount of potential events quickly and accurately.
Approach: They propose a QE system that uses a four-stage event retrieval framework . they collect news headlines and then refine a dual-tower semantic model to serve as an encoder .
Outcome: The proposed system can select the best expansion from a significant amount of potential events quickly and accurately.

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