[PDF] Top 20 Constrained Grammatical Error Correction using Statistical Machine Translation

... Exploring grammatical error correction with not-so-crummy machine ...Applications Using NLP, pages 44–53, Montreal, ...Japanese Error Correction of Second Lan- guage ... See full document

... phrase-based statistical machine trans- lation (Rozovskaya and Roth, 2016; Junczys- Dowmunt and Grundkiewicz, 2016; Chollampatt and Ng, ...chine translation (Yuan and Briscoe, 2016; Xie et ... See full document

... Junczys-Dowmunt and Grundkiewicz (2016) no- ticed that when tuning on the entire NUCLE cor- pus, even better results can be achieved if the error rate of NUCLE is adapted to the error rate of the original ... See full document

... tions remain. We further divide the data into four folds. Each folds serves as development set for pa- rameter tuning, while the three remaining parts are treated as translation model training data. The full ... See full document

... To date, most work in grammatical error cor- rection has focused on targeting specific er- ror types. We present a probe study into whether we can use round-trip translations ob- tained from Google ... See full document

... few error types, the CoNLL-2014 shared task dealt with correction of all kinds of textual ...Neural machine translation ap- proaches have also showed some promise (Xie et ... See full document

... Grammatical error correction, like other ma- chine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to ...realistic ... See full document

... one error is still present in the post-edited ...of error in the post-edited text and need to be handled ...of translation is higher as compared to other ...their translation corresponding to ... See full document

... multi-word translation units from phrase- based SMT into a transfer system for dependency structure ...The statistical components of our system are modeled on the phrase-based sys- tem of Koehn et ... See full document

... (2018) using an ensemble of neural Trans- former models (Vaswani et ...neural machine transla- tion (Luong and Manning, 2015), it has not been presented in GEC thus far, perhaps due to the ab- sence of such ... See full document

... achieved using MERT-flat, so rather than tune from flat parameters in a fixed fast setting, we conclude that it is better to: (1) use MERT to find feature weights in slow settings; (2) optimize decoder parameters ... See full document

... The main reason why we choose the LG parser is that it provides a robustness feature: null-link scheme. The null-link scheme allows the parser to parse a sentence even when the parser can not fully interpret the entire ... See full document

... classification, statistical machine translation (SMT), and neural machine translation (NMT) based systems were built for the GEC ...results using a big lan- guage model and some ... See full document

... out-of-the-box machine translation toolk- its like Moses (Koehn et ...cal error correction seems ...grammar correction system, the standard features and opti- mization methods are ... See full document

... Heafield et al. (2009) proposed a system combi- nation method for machine translation that is sim- ilar to that of reranking. System combination is a method that merges the outputs of multiple systems to ... See full document

... From the results in Table 1, we can see that NMT- based systems alone are not able to achieve compa- rable results to an SMT-based system. It is proba- bly because of the rare word problem, as increasing the source side ... See full document

... Chinese grammatical error diagnosis (CGED). We use a statistical machine translation method already ap- plied to several similar tasks (Brockett et ...alternative translation ... See full document

... on grammatical error correction ...possible error types in a real-life environment, we propose a factored statistical machine translation (SMT) model for this ...consider ... See full document

... Training without supervision is possible in the classification framework, as follows. For a given mistake type, e.g. preposition, a classifier is trained on native data that is assumed to be cor- rect; the classifier ... See full document

... scoring translation rules, it can work on any aligned text, and is sim- ilar to the forthcoming ERRANT toolkit, which is uses a rule-based framework for automatically cat- egorizes grammatical edits (Bryant ... See full document