This project constructs a database, annotates a corpus, and evaluates models for recognizing classical allusions in Chinese texts, supporting humanities research and language education.
L2C-Rater is an automated essay scoring tool that leverages 90 linguistic features and ordinal logistic regression to accurately evaluate Chinese as a second language (L2) essays, providing a robust, interpretable solution for language learners and educators.
This project introduces a model-agnostic diagnostic method using diverse counterfactual interventions to interpret neural-based Automated Essay Scoring models, revealing their decision-making processes and alignment with human scoring rubrics, while offering potential applications across multiple AI domains. 
Published in Lecture Notes in Artificial Intelligence, 2021
We propose a method for automated scoring of Chinese L2 essays, specifically HSK test essays, using 90 linguistic features and an Ordinal Logistic Regression model, achieving high accuracy and offering insights for writing feedback. The source code of the project is available at GitHub, and a demo of the project is available at Demo.
Recommended citation: Wang, Y., & Hu, R. (2021, August). A prompt-independent and interpretable automated essay scoring method for Chinese second language writing. In China National Conference on Chinese Computational Linguistics (pp. 450-470). Cham: Springer International Publishing.
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Published in ACL Anthology: Findings of EMNLP, 2024
We propose a counterfactual intervention method using Large Language Models (LLMs) to reveal that, in automated essay scoring, while BERT-like models focus on sentence-level features, LLMs align more comprehensively with scoring rubrics by emphasizing conventions, accuracy, language complexity, and organization. The source code and data of this paper is available at GitHub.
Recommended citation: Yupei Wang, Renfen Hu, and Zhe Zhao. 2024. Beyond Agreement: Diagnosing the Rationale Alignment of Automated Essay Scoring Methods based on Linguistically-informed Counterfactuals. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8906–8925, Miami, Florida, USA. Association for Computational Linguistics.
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Undergraduate Course (Spring), Beijing Normal University, 2023
Served as a teaching assistant for Professor Renfen Hu’s Python Programming and Data Analysis course, covering fundamental knowledge of Python programming, data analysis, and machine learning.
Graduate Course (Autumn), Beijing Normal University, 2023
Served as a teaching assistant for Professor Renfen Hu’s Object-Oriented Programming and Natural Language Processing course.
Graduate Course (Spring), Beijing Normal University, 2024
Serving as a teaching assistant for Professor Renfen Hu’s Natural Language Processing course.
Undergraduate Course (Spring), Beijing Normal University, 2024
Continued serving as a teaching assistant for Professor Renfen Hu’s Python Programming and Data Analysis course.