Papers by Yangfan Zhang

4 papers
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)

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Challenge: Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training.
Approach: They propose a framework that enhances zero-shot slot inference through robust prompt alignment.
Outcome: Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST.
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.
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)

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Challenge: Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions.
Approach: They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level.
Outcome: The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods.
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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