[PDF] Top 20 Exploring Grammatical Error Correction with Not So Crummy Machine Translation

... 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

... We compare our results with the well-known GEC systems, as shown in Table 4. Rule, classification, statistical machine translation (SMT), and neural machine translation (NMT) based systems ... See full document

... Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, and Alexandra Birch. 2018. ... See full document

... The NUCLE corpus matches the domain of the CoNLL benchmarks perfectly. It is however much smaller than the Lang-8 corpus. A setting like this seems to be a good fit for domain-adaptation techniques. Sennrich et al. ... 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 models ... See full document

... Operation Sequence Model. Durrani et al. (2013) introduce Operation Sequence Models in Moses. These models are Markov translation models that in our setting can be interpreted as Markov edition models. ... See full document

... learner’s corpora that were scraped from the so- cial language learning site Lang-8 (http:// lang-8.com). For our first experiments we use entries from “Lang-8 Learner Corpora v1.0” with English as the learned ... See full document

... To capture fluency as well as grammaticality in evaluation on such references, we use GLEU as the reward. We have shown GLEU to be more strongly preferred than other GEC metrics by na- tive speakers (Sakaguchi et al., ... See full document

... tasks, grammatical error correction metrics must be evaluated against ground ...a grammatical correction, together with the fact that the use case for grammatically-corrected output is ... See full document

... CoNLL-2014 was reported by Junczys-Dowmunt et al. (2018) using an ensemble of neural Trans- former models (Vaswani et al., 2017), where the decoder side of each model is pretrained as a lan- guage model. From a modeling ... 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

... Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in text written by non-native English ...building machine learning classifiers for spe- ... 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

... to grammatical error correc- tion developed rule-based systems or classifiers targeting specific error types such as prepositions or determiners, ...in machine translation, though some ... See full document

... We also observed that the system was bi- ased towards making unnecessary insertions of the definite article before some specific nouns. This means that the system would almost always change words like cost, elderly or ... See full document

... tween the results of baseline and reranking using our features was statistically significant (p < 0.01). Be- cause a large N-gram language model was adopted for reranking, recall increased considerably but pre- cision ... See full document

... monolingual translation tasks, in which much of the source sen- tence should remain unchanged (Xu et ...the correction with the reference and penalizes unchanged n-grams in the correction that are ... See full document

... In this paper, we present a novel approach to the GEC task using meta-learning. We focus mainly on article errors for two reasons. First, articles are one of the most significant sources of GE for the learners with ... See full document

... In this section, we describe our error generating method. For each sentence, we assign a proba- bility distribution (as shown in Table 4) to deter- mine the number of errors according to the sen- tence length. The ... See full document

... annotated errors is much higher in the test set than in the development set: 46% of clauses have cor- rections. It has been found previously that a low frequency of errors increase the difficulty of the correction ... See full document