Papers by Ran Lu
Event Extraction as Question Generation and Answering (2023.acl-short)
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| Challenge: | Recent work on Event Extraction addresses the error propagation issue found in token-based classification approaches. |
| Approach: | They propose a Question Generation (QG) model that generates questions that leverage contextual information instead of fixed templates. |
| Outcome: | The proposed model outperforms all previous single-task-based models on the ACE05 English dataset. |
RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository (2026.acl-long)
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Zhiyuan Peng, Xin Yin, Pu Zhao, Fangkai Yang, Lu Wang, Ran Jia, Xu Chen, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing benchmarks focus on isolated function/class-level generation, neglecting complete microservice repository generation. |
| Approach: | They propose a multilingual benchmark for repository-level end-to-end web microservice generation that reflects real-world development workflows. |
| Outcome: | The benchmark compared 106 repositories across 18 domains and 11 frameworks and 1,258 API endpoints and 2,335 test cases. |
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics (2023.acl-long)
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Liang Ma, Shuyang Cao, Robert L Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, but they are insufficient for diagnosing whether metrics are consistent, effective on human-written texts, and sensitive to different error types. |
| Approach: | They propose to use unfaithful minimal pairs to measure the consistency of automatic faithfulness metrics by comparing human-written summary pairs with a dataset of 889 human-writing, minimally different summary pairs. |
| Outcome: | The proposed benchmarks show that the most discriminative metrics tend not to be the most consistent, and that the best performing metrics are sensitive to errors. |
Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)
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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu
| Challenge: | Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance. |
| Approach: | They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior. |
| Outcome: | The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines. |
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time (2026.acl-long)
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| Challenge: | Existing TTRL methods rely on positive pseudo-labeling strategies to enhance reasoning capabilities. |
| Approach: | They propose a test-time reinforcement learning framework that mitigates label noise amplification by deriving pseudo-rewards from majority voting consensus. |
| Outcome: | The proposed framework mitigates label noise amplification by implementing selective positive pseudo-labeling and entropy-gated negative p-labeled pruning. |
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)
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| Challenge: | Existing methods for solving geometric problems are either small in scale or not publicly available. |
| Approach: | They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning. |
| Outcome: | The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step. |
A New Task and Dataset on Detecting Attacks on Human Rights Defenders (2023.findings-acl)
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| Challenge: | Existing datasets for event extraction cannot extract information from textual sources. |
| Approach: | They propose to use crowdsourced annotations on 500 online news articles to train and evaluate baseline models to predict annotated characteristics. |
| Outcome: | The proposed dataset includes crowdsourced annotations on 500 online news articles and includes fine-grained information about the type and location of attacks, as well as information about victims. |
CEHA: A Dataset of Conflict Events in the Horn of Africa (2025.coling-main)
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Rui Bai, Di Lu, Shihao Ran, Elizabeth M. Olson, Hemank Lamba, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing datasets categorizing conflict events do not cover all of the fine-grained types of conflict relevant to areas like the Horn of Africa. |
| Approach: | They propose to use online news articles to categorize violent conflict events . they propose to extract event-relevance and event-types from 500 English event descriptions . |
| Outcome: | The proposed dataset categorizes conflict risk according to specific areas required by stakeholders in the Humanitarian-Peace-Development Nexus. |