Papers by Hannan Cao
A Constrained Text Revision Agent via Iterative Planning and Searching (2025.findings-acl)
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| Challenge: | Existing text revision systems are capable of generating fluent and coherent text, but struggle with constrained text revision (CTR). |
| Approach: | They propose a tool that generates revisions tailored to different scenarios using a planner, a reviser and adaptable tools. |
| Outcome: | The proposed agent outperforms baseline approaches in both constraint adherence and revision quality. |
Mitigating Exposure Bias in Grammatical Error Correction with Data Augmentation and Reweighting (2023.eacl-main)
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| Challenge: | Existing approaches to grammatical error correction (GEC) use sequence-to-sequence models, but there is an exposure bias problem. |
| Approach: | They propose a data manipulation approach to overcome the exposure bias problem in seq2seq GEC . they propose augmentation methods to mimic decoder input and reweighting methods to automatically balance the importance of each kind of augmented samples. |
| Outcome: | The proposed method improves on benchmark GEC datasets. |
Grammatical Error Correction with Contrastive Learning in Low Error Density Domains (2021.findings-emnlp)
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| Challenge: | grammatical error correction (GEC) is a text generation task . performance on low error density domains where texts written by native speakers can be improved. |
| Approach: | They propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate. |
| Outcome: | The proposed approach significantly improves the performance of GEC models in low error density domains. |
Rationalize and Align: Enhancing Writing Assistance with Rationale via Self-Training for Improved Alignment (2025.findings-acl)
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| Challenge: | Existing writing assistants rely on supervised fine-tuning to optimize models for multiple revisions. |
| Approach: | They propose a framework that enhances WA performance with rationale and alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art WAs and the closed-source GPT-4o by 3.9 and 7.1 points on average across eight well-established writing-related test sets. |
Unsupervised Grammatical Error Correction Rivaling Supervised Methods (2023.emnlp-main)
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| Challenge: | Current state-of-the-art grammatical error correction systems rely on labeled data . current systems require manual correction and require a large quantity of labeles . |
| Approach: | They propose an unsupervised method to build a grammatical error correction system using a fixer and a critic. |
| Outcome: | The proposed system outperforms previous unsupervised systems on English and Chinese GEC. |