Papers by Huaiyu Zhu
A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels (2020.findings-emnlp)
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| Challenge: | Prior attempts to develop crowdsourcing methods have either had low accuracy or required substantial expert annotation. |
| Approach: | They propose a multi-stage crowd workflow that reduces expert involvement without sacrificing accuracy. |
| Outcome: | The proposed method reduces expert effort by 4x, from 56% to 14% of cases. |
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Existing methods for training one model on multiple languages outperform monolingual baselines for low resource languages. |
| Approach: | They propose a method to combine training data from multiple languages to create a shared representation space for the model. |
| Outcome: | The proposed method outperforms monolingual and polyglot training on low resource languages. |
Universal Proposition Bank 2.0 (2022.lrec-1)
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Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li
| Challenge: | Semantic role labeling (SRL) is a shallow semantic parsing task that identifies "who did what to whom when, where etc." SRL is useful in a wide range of downstream NLP tasks and real-world applications. |
| Approach: | They propose a method to generate shallow semantic parsing tasks using monolingual SRL and multilingual parallel data. |
| Outcome: | The proposed method improves the quality of the generated propbanks. |
SystemT: Declarative Text Understanding for Enterprise (N18-3)
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| Challenge: | a growing number of enterprise applications are relying on text understanding systems to understand information in unstructured and semi-structured forms. |
| Approach: | They propose a declarative text understanding system that addresses these challenges . they summarize the impact of SystemT on business and education . |
| Outcome: | The system addresses the challenges of enterprise text understanding systems . it has been deployed in a wide range of enterprise applications . |
PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation (2023.findings-eacl)
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Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li
| Challenge: | Existing evaluation scripts for semantic role labeling do not consider error propagation . existing evaluation script does not consider argument independent of predicate sense . |
| Approach: | They propose a more strict SRL evaluation metric PriMeSRL to address these issues . they propose to use a metric that measures the quality of the underlying SRL models . |
| Outcome: | The proposed metric reduces quality evaluation of all SoTA SRL models and penalizes failures. |
Development of an Enterprise-Grade Contract Understanding System (2021.naacl-industry)
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Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
| Challenge: | Currently, legal contract review remains an expensive and arduous process. |
| Approach: | They describe a commercial system designed and deployed for contract understanding that enables legal professionals to review contracts. |
| Outcome: | The proposed system is used by a wide range of enterprise users and solves three major challenges. |
Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models (2025.findings-emnlp)
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| Challenge: | Existing noise detection techniques for autoencoder models do not generalize to ArLMs due to differences in learning dynamics. |
| Approach: | They propose a method that leverages training dynamics to rank datapoints from easy-to-learn to hard-tolear . TDRanker achieves at least 2x faster denoising than previous techniques . |
| Outcome: | The proposed method demonstrates robustness across multiple model architectures and noise levels. |
Small but Mighty: New Benchmarks for Split and Rephrase (2020.emnlp-main)
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| Challenge: | Split and Rephrase is a text simplification task that requires a strong evaluation benchmark and metric . despite its relatively new nature, the benchmark dataset contains easily exploitable syntactic cues . |
| Approach: | They propose to use crowdsourced datasets to evaluate split and rephrase models . they find that the widely used benchmark dataset universally contains exploitable syntactic cues . |
| Outcome: | The proposed model performs better than the state-of-the-art model, the authors say . they show that the datasets contain significantly more diverse syntax . |