Papers by Leyang Yang

Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)

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Yuejiao Fei, Leyang Cui, Sen Yang, Wai Lam, Zhenzhong Lan, Shuming Shi

Challenge:	To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors.
Approach:	They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections.
Outcome:	The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule.

Investigating Non-local Features for Neural Constituency Parsing (2022.acl-long)

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Leyang Cui, Sen Yang, Yue Zhang

Challenge:	Constituency parsers have been able to achieve competitive performance by using local features.
Approach:	They propose to inject non-local features into the training process of a local span-based parser by predicting constituent n-gram non-local patterns and ensuring consistency between constituents and local constituents.
Outcome:	The proposed method outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings.

Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal (2024.acl-long)

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Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, Jinsong Su

Language Models

Challenge:	Existing methods to train LLMs on previous training data are not feasible in real-world applications because of catastrophic forgetting.
Approach:	They propose a framework that uses the LLM to generate synthetic instances for rehearsal and refine the instance outputs based on the synthetic inputs.
Outcome:	The proposed framework achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient.

Template-Based Named Entity Recognition Using BART (2021.findings-acl)

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Leyang Cui, Yu Wu, Jian Liu, Sen Yang, Yue Zhang

Language Models

Challenge:	Existing methods for fewshot NER do not make full use of knowledge transfer in NER model parameters.
Approach:	They propose a template-based method for NER that treats NER as a language model ranking problem in a sequence-to-sequence framework.
Outcome:	The proposed method achieves 92.55% F1 score on the CoNLL03 task and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 scores on the MIT Movie, the ATIS, and the MATLAB task.

Cross-domain Generalization for AMR Parsing (2022.emnlp-main)

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Xuefeng Bai, Sen Yang, Leyang Cui, Linfeng Song, Yue Zhang

Semantic Text Processing

Challenge:	Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input.
Approach:	They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing.
Outcome:	The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets.

FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)

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Linyi Yang, Lifan Yuan, Leyang Cui, Wenyang Gao, Yue Zhang

Low-Resource NLP

Challenge:	Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples.
Approach:	They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain.
Outcome:	The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods.

Challenges to Open-Domain Constituency Parsing (2022.findings-acl)

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Sen Yang, Leyang Cui, Ruoxi Ning, Di Wu, Yue Zhang

Challenge:	Existing findings on cross-domain constituency parsing are only made on a limited number of domains.
Approach:	They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features.
Outcome:	The proposed model significantly improves the performance of the proposed model on the domain-variant features.

MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)

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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang

Language Models

Challenge:	Existing research has focused on evaluating detection methods for specific domains or language models.
Approach:	They build a testbed to detect texts from diverse human writings and LLMs using different detection methods.
Outcome:	Empirical results show that the top performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.

What Have We Achieved on Text Summarization? (2020.emnlp-main)

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Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang

Information Extraction & Text Mining

Challenge:	Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals.
Approach:	They analyze 8 major sources of errors on 10 representative summarization models manually.
Outcome:	Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models.

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

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Ziwei Wang, Junjie Zheng, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Fang Zhouhua, Zhiwei Liu, Dajun Chen, Yong Li, Jiajun Bu

Challenge:	Advanced GUI agents suffer from prohibitive deployment costs on resource-constrained devices.
Approach:	They propose a lightweight GUI agent with GUI-specific knowledge and task scalability . LAMO-3B supports monolithic execution and MAS-style orchestration .
Outcome:	The proposed GUI agent LAMO-3B supports monolithic execution and MAS-style orchestration.

Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs (2025.findings-naacl)

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Sen Yang, Xin Li, Leyang Cui, Lidong Bing, Wai Lam

Explainability & Interpretability in NLP

Challenge:	a new framework for complex reasoning with LLMs is developed to improve reasoning proof accuracy and interpretability.
Approach:	They propose to use LLMs to generate search logs that can be interpreted into human-readable reasoning proofs.
Outcome:	The proposed framework improves reasoning accuracy but lacks interpretability due to black-box nature of the solvers.

Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning (2024.findings-emnlp)

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Sen Yang, Leyang Cui, Deng Cai, Xinting Huang, Shuming Shi, Wai Lam

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) have shown remarkable capabilities to understand and generate human languages, supporting applications such as question answering, coding, and psychological counseling.
Approach:	They propose strategies to save annotation budgets while achieving competitive or even better performances for iterative preference learning.
Outcome:	The proposed methods save annotation budgets while achieving better performance.

Making the Best Use of Review Summary for Sentiment Analysis (2020.coling-main)

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Sen Yang, Leyang Cui, Jun Xie, Yue Zhang

Sentiment Analysis

Challenge:	Existing methods for sentiment analysis of user reviews are limited to a few examples.
Approach:	They propose a hierarchically-refined attention model that exploits the sentimental distribution of a review and its corresponding summary.
Outcome:	The proposed model can make better use of user-written summaries for review sentiment analysis and is more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.