  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.
