Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction
arXiv:2010.03260
Abstract
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.
arXiv commentAccepted by EMNLP 2020
References in corpus (13)
- Adam: A Method for Stochastic Optimization
- Sequence to Sequence Learning with Neural Networks
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Understanding Back-Translation at Scale
- Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study
- Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data
- Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting
- An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
- JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
- Improving Grammatical Error Correction with Machine Translation Pairs
- Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
- Parallel Iterative Edit Models for Local Sequence Transduction
- Encode, Tag, Realize: High-Precision Text Editing