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

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.

Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation

“…For example, GECToR outperforms baseline models on English GEC datasets, but its performance severely degenerates on the Chinese GEC task, as shown in Table 3. There also exist methods that integrate seq2seq models with sequence tagging methods into a pipeline system to improve the performance or efficiency (Chen et al 2020;Hinson, Huang, and Chen 2020), which cannot be optimized endto-end.…”

Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding

“…We follow recent work in English GEC to conduct experiments in the restricted training setting of BEA-2019 GEC shared task (Bryant et al, 2019): We use Lang-8 Corpus of Learner English (Mizumoto et al, 2011), NUCLE (Dahlmeier et al, 2013), FCE (Yannakoudakis et al, 2011) and W&I+LOCNESS (Granger;Bryant et al, 2019) as our GEC training data. 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, 2014 dataset that contains 1,312 sentences as our main test set, and evaluate the speedup as well as Max-Match (Dahlmeier and Ng, 2012) precision, recall and F 0.5 using their official evaluation scripts 4 . For validation, we use CoNLL-2013 that contains 1,381 sentences as our validation set.…”

“…Table 5: The performance and online inference efficiency evaluation of efficient GEC models in CoNLL-14. For the models with , their performance and speedup numbers are from Chen et al (2020) who evaluate the online efficiency in the same runtime setting (e.g., GPU and runtime libraries) with ours. The underlines indicate the speedup numbers of the models are evaluated with Tensorflow based on their released codes, which are not strictly comparable here.…”

“…However, its performance is not so desirable as its seq2seq counterpart despite its high efficiency. The only two previous efficient approaches that are both languageindependent and good-performing are Stahlberg and Kumar (2020) which uses span-based edit operations to correct sentences to save the time for copying unchanged tokens, and Chen et al (2020) which first identifies incorrect spans with a tagging model then only corrects these spans with a generator. However, all the approaches have to extract edit operations and even conduct token alignment in advance from the error-corrected sentence pairs for training the model.…”

“…The Transformer (Vaswani et al, 2017) has become the most popular model for Grammatical Error Correction (GEC). In practice, however, the sequenceto-sequence (seq2seq) approach has been blamed recently (Chen et al, 2020;Stahlberg and Kumar, 2020;Omelianchuk et al, 2020) for its poor inference efficiency in modern writing assistance applications (e.g., Microsoft Office Word 1 , Google Docs 2 and Grammarly 3 ) where a GEC model usually performs online inference, instead of batch inference, for proactively and incrementally checking a user's latest completed sentence to offer instantaneous feedback.…”