# CSED: A Chinese Semantic Error Diagnosis Corpus

arXiv: https://arxiv.org/abs/2305.05183 · HTML: https://mlsanity.com/paper/2305.05183/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://mlsanity.com/paper/2109.05729/) (2021)
- [Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees](https://mlsanity.com/paper/2103.04350/) (2021)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2210.12484/) (2022)
- [Improving BERT with Syntax-aware Local Attention](https://mlsanity.com/paper/2012.15150/) (2020)
- [General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining](https://mlsanity.com/paper/2203.10929/) (2022)

## Built on by (most cited first, 3 of 3)

- [LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2403.17413/) (2024)
- [System Report for CCL24-Eval Task 7: Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation](https://mlsanity.com/paper/2407.08206/) (2024)
- [VAA-CSEC: Vote-guided Advantage Allocation for Chinese Semantic Error Correction](https://mlsanity.com/paper/2609.36804/) (2026)

## Abstract (arXiv)

Recently, much Chinese text error correction work has focused on Chinese Spelling Check (CSC) and Chinese Grammatical Error Diagnosis (CGED). In contrast, little attention has been paid to the complicated problem of Chinese Semantic Error Diagnosis (CSED), which lacks relevant datasets. The study of semantic errors is important because they are very common and may lead to syntactic irregularities or even problems of comprehension. To investigate this, we build the CSED corpus, which includes two datasets. The one is for the CSED-Recognition (CSED-R) task. The other is for the CSED-Correction (CSED-C) task. Our annotation guarantees high-quality data through quality assurance mechanisms. Our experiments show that powerful pre-trained models perform poorly on this corpus. We also find that the CSED task is challenging, as evidenced by the fact that even humans receive a low score. This paper proposes syntax-aware models to specifically adapt to the CSED task. The experimental results show that the introduction of the syntax-aware approach is meaningful.