# System Report for CCL24-Eval Task 7: Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation

arXiv: https://arxiv.org/abs/2407.08206 · HTML: https://mlsanity.com/paper/2407.08206/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation](https://mlsanity.com/paper/2205.10884/) (2022)
- [An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages](https://mlsanity.com/paper/2109.00165/) (2021)
- [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://mlsanity.com/paper/2305.16023/) (2023)
- [BBScore: A Brownian Bridge Based Metric for Assessing Text Coherence](https://mlsanity.com/paper/2312.16893/) (2023)
- [CSED: A Chinese Semantic Error Diagnosis Corpus](https://mlsanity.com/paper/2305.05183/) (2023)
- [Combining GCN and Transformer for Chinese Grammatical Error Detection](https://mlsanity.com/paper/2105.09085/) (2021)
- [Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring](https://mlsanity.com/paper/2211.13373/) (2022)

## Abstract (arXiv)

This system report presents our approaches and results for the Chinese Essay Fluency Evaluation (CEFE) task at CCL-2024. For Track 1, we optimized predictions for challenging fine-grained error types using binary classification models and trained coarse-grained models on the Chinese Learner 4W corpus. In Track 2, we enhanced performance by constructing a pseudo-dataset with multiple error types per sentence. For Track 3, where we achieved first place, we generated fluency-rated pseudo-data via back-translation for pre-training and used an NSP-based strategy with Symmetric Cross Entropy loss to capture context and mitigate long dependencies. Our methods effectively address key challenges in Chinese Essay Fluency Evaluation.