Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction

By M. Chen, Tao Ge, Xingxing Zhang, Furu Wei, M. Zhou. 2020

48 citations recorded by the source corpus.

We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.

Subfields: Artificial Intelligence, Natural Language Processing, Efficiency & Compression

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tao Ge
2. Furu Wei
3. Xin Sun
4. Heming Xia
5. Zhifang Sui
6. Houfeng Wang
7. Zhuowei Liu
8. Peiyi Wang

In Natural Language Processing

1. Tao Ge
2. Furu Wei
3. Xin Sun
4. Houfeng Wang
5. Zhuowei Liu
6. Heming Xia
7. Zhifang Sui
8. Peiyi Wang

In Efficiency & Compression

1. Tao Ge
2. Furu Wei
3. Canwen Xu
4. Julian McAuley

Citations in the corpus

• Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding (2021, methodology): “For facilitating fair comparison in the efficiency evaluation, we follow the previous studies (Omelianchuk et al., 2020; Chen et al., 2020) which conduct GEC efficiency evaluation to use CoNLL-2014 (Ng et al.”
• Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation (2022, result): “…(Ge et al., 2022b; Yang et al., 2023) present extensive empirical studies of Speculative Decoding in special cases (e.g., text editing or retrieval-augmented generation) and compare it with competitive approaches (Malmi et al., 2019; Omelianchuk et al., 2020; Chen et al., 2020) in those scenarios.”
• A Comprehensive Survey of Grammatical Error Correction (2021, background): “The most recent research on token-level GED-assisted GEC [15] achieves comparable performance to the state-of-the-art performance with a significant reduction in inference time.”