[PDF] Top 20 Memory based Grammatical Error Correction
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Memory based Grammatical Error Correction
... grammar correction system based on five memory-based classifiers implementing eight different error ...The error correctors make use of information from a lemmatizer and a noun ... See full document
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Phrase based Machine Translation is State of the Art for Automatic Grammatical Error Correction
... metric. For inherently weak systems this can result in all correction attempts to be disabled, MERT then learns to disallow all changes since they lower the similarity to the reference as determined by BLEU. ... See full document
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Grammatical Error Correction: Machine Translation and Classifiers
... Learner error patterns: Error patterns are ex- tracted from learner data and “injected” into mod- els trained on native data (Rozovskaya and Roth, ...are based on native data. (2) Learner ... See full document
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RACAI GEC – A hybrid approach to Grammatical Error Correction
... treat error detec- tion and error correction separately, in a two- stage ...the error correction process and serve as common denominators for different classes of ... See full document
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Grammatical Error Correction with Alternating Structure Optimization
... We present a novel approach to grammatical error correction based on Alternating Struc- ture Optimization. As part of our work, we introduce the NUS Corpus of Learner En- glish (NUCLE), a ... See full document
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A Meta Learning Approach to Grammatical Error Correction
... A meta-classifier decides the final output based on the intermediate results obtained from several base classifiers. Each base classifier is trained on a different corpus than are the other classifiers. In this ... See full document
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Language Model Based Grammatical Error Correction without Annotated Training Data
... Since the end of the CoNLL-2014 shared task on grammatical error correction (GEC), re- search into language model (LM) based ap- proaches to GEC has largely stagnated. In this paper, we ... See full document
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Automatic Metric Validation for Grammatical Error Correction
... BLEU. BLEU (Papineni et al., 2002) is a reference-based metric that averages the output- reference n-gram overlap precision values over different ns. While commonly used in MT and other text generation tasks ... See full document
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Erroneous data generation for Grammatical Error Correction
... In this work, we present a novel erroneous data generating method for training English GEC mod- els. Our experiments show that Transformer mod- els pretrained on generated corpus significantly outperform the previous GEC ... See full document
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Grammatical Error Correction with Neural Reinforcement Learning
... Baselines In addition to our MLE baseline, we compare four leading GEC systems. All the sys- tems are based on SMT, but they take different approaches. The first model, proposed by Felice et al. (2014), uses a ... See full document
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Joint Learning and Inference for Grammatical Error Correction
... contains a good number of interacting errors – ar- ticle, noun, and verb agreement mistakes – makes the data set well-suited for studying which approach works best for addressing interacting phenomena. The HOO-2011 ... See full document
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Human Evaluation of Grammatical Error Correction Systems
... In this section, it is our aim to produce a system ranking from best to worse by computing the av- erage number of times each system was judged better than other systems based on the collected pairwise rankings. ... See full document
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Grammatical Error Correction in Low Resource Scenarios
... in grammatical error correction (GEC) in ...single error-type classi- fiers and their combinations were due to larger amount of data surpassed by statistical and later by neural machine ... See full document
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Generating artificial errors for grammatical error correction
... correct grammatical errors and context- sensitive spelling mistakes in English and ...one error template, thereby generating many pairs for the same original ... See full document
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Neural Grammatical Error Correction with Finite State Transducers
... lem as corrections tend to be very local, and lexical choices are usually limited. Finite state transduc- ers (FSTs) are an efficient way to represent large structured search spaces. In this paper, we propose to ... See full document
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The BEA 2019 Shared Task on Grammatical Error Correction
... For example, Shuyao, UEDIN-MS and Kakao&Brain respectively trained their systems on 145 million, 100 million and 45 million artificial sentences, while CAMB-CUED instead concentrated on optimising the ratio of ... See full document
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Grammatical Error Correction Considering Multi word Expressions
... matical error correction is targeted on one or few restricted types of learners’ ...of grammatical errors (Mizu- moto et ...errors, grammatical error correction methods using ... See full document
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Systematically Adapting Machine Translation for Grammatical Error Correction
... GLEU or the un-coded JFLEG corpus. To investi- gate the types of changes systems make on a more granular level, we apply the feature extraction method described in Section 3.2 to quantify the morphological and lexical ... See full document
12
Building a State of the Art Grammatical Error Correction System
... The Illinois system implements two verb modules – agreement and form – that consist of the following components: (1) candidate identification; (2) deter- mining the relevant module for each candidate based on verb ... See full document
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Grammatical error correction using neural machine translation
... 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 ... See full document
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