You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models. GEC models have been previously evaluated based on a single commonly applied corpus: the CoNLL-2014 benchmark. However, the evaluation remains incomplete because the task difficulty varies depending on the test corpus and conditions such as the proficiency levels of the writers and essay topics. To overcome this limitation, we evaluate the performance of several GEC models, including NMT-based (LSTM, CNN, and transformer) and an SMT-based model, against various learner corpora (CoNLL-2013, CoNLL-2014, FCE, JFLEG, ICNALE, and KJ). Evaluation results reveal that the models' rankings considerably vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models.

Cited Abstract(s):
[1]: Abstract : As a result of this grant, the researchers have now published oil CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, with over 3 million words of that material assigned skelet al grammatical structure. This material now includes a fully hand-parsed version of the classic Brown corpus. About one half of the papers at the ACL Workshop on Using Large Text Corpora this past summer were based on the materials generated by this grant.

[2]: We describe the OntoNotes methodology and its result, a large multilingual richly-annotated corpus constructed at 90 interannotator agreement. An initial portion (300K words of English newswire and 250K words of Chinese newswire) will be made available to the community during 2007.

[3]: We describe a shared task on parsing web text from the Google Web Treebank. Participants were to build a single parsing system that is robust to domain changes and can handle noisy text that is commonly encountered on the web. There was a constituency and a dependency parsing track and 11 sites submitted a total of 20 systems. System combination approaches achieved the best results, however, falling short of newswire accuracies by a large margin. The best accuracies were in the 80-84 range for F1 and LAS; even part-ofspeech accuracies were just above 90 .
We are motivated by the issue of robustness in the parsing community. This field previously focused on improving parsing accuracy on Penn Treebank [1] . However, robustness was largely improved by evaluation using multiple corpora including Ontonotes [2] and Google Web Treebank [3] . A situation similar to this might also occur in GEC. In other words, evaluation in GEC has relied heavily on the CoNLL-2014 benchmark, which implies that the field is overdeveloping on this dataset.