[PDF] Top 20 Neural and FST based approaches to grammatical error correction
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Neural and FST based approaches to grammatical error correction
... Artificial error generation is the process of in- jecting artificial errors into a set of error-free sen- ...ual error annotation of learner data is a slow and expensive process, artificial ... See full document
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Approaching Neural Grammatical Error Correction as a Low Resource Machine Translation Task
... in neural machine translation and has been demonstrated to be generally superior to UNK-replacement ...of grammatical error correction even when word segmentation issues have been explored (Ji ... See full document
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Neural Grammatical Error Correction with Finite State Transducers
... Grammatical error correction (GEC) is the task of automatically correcting all types of errors in text; ...Using neural models for GEC is be- coming increasingly popular (Xie et ...purely ... See full document
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Fluency Boost Learning and Inference for Neural Grammatical Error Correction
... the neural sequence-to-sequence (seq2seq) models for grammatical error correction (GEC) have two limitations: (1) a seq2seq model may not be well gen- eralized with only limited ... See full document
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A Nested Attention Neural Hybrid Model for Grammatical Error Correction
... hybrid neural model with two nested levels of attention: word-level and ...the grammatical error correction task and achieves the best reported results on the CoNLL-14 benchmark among fully ... See full document
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Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models
... model error types specifi- cally, nor does it require linguistic analysis like pars- ing and part-of-speech (POS) ...a neural network joint model (NNJM) (Devlin et ... See full document
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Grammatical error correction using neural machine translation
... Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in text written by non-native English ...cific error types ...all error types ... See full document
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Neural Grammatical Error Correction Systems with Unsupervised Pre training on Synthetic Data
... synthetic error gen- eration method based on confusion sets generated from an inverted ...matical error correction ...genuine error-annotated ESL learner data is ... See full document
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Grammatical Error Correction with Neural Reinforcement Learning
... a neural encoder-decoder GEC model with a reinforcement learning approach in which we directly optimize the model toward our final objective ...the neural reinforce- ment learning model (NRL) is to maximize ... See full document
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Neural Sequence Labelling Models for Grammatical Error Correction
... Grammatical Error Correction (GEC) in non- native text attempts to automatically detect and correct errors that are typical of those found in learner ...(SMT) approaches to GEC have at- ... See full document
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Corpora Generation for Grammatical Error Correction
... Grammatical Error Correction (GEC) has been recently modeled using the sequence- to-sequence ...that neural GEC models trained using either type of corpora give similar per- ...two ... See full document
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Improving Grammatical Error Correction via Pre Training a Copy Augmented Architecture with Unlabeled Data
... proper neural ar- chitecture is needed for it. We enhance the current neural architecture by enabling it to copy the un- changed words and the out-of-vocabulary words directly from the source sentence, just ... See full document
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Cross Sentence Grammatical Error Correction
... Automatic grammatical error correction (GEC) research has made remarkable progress in the past ...existing approaches and improve strong neural encoder-decoder models by appropriately ... See full document
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Erroneous data generation for Grammatical Error Correction
... in neural Grammatical Error Correction (GEC) sys- tems can significantly improve the system ...art neural GEC system is an ensemble of four Transformer models pretrained on a large ... See full document
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Minimally Augmented Grammatical Error Correction
... Most neural approaches to automatic grammati- cal error correction (GEC) require error-labelled training data to achieve their best ... See full document
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Grammatical Error Correction: Machine Translation and Classifiers
... two approaches of adding supervision: (1) Learner error patterns: Error patterns are ex- tracted from learner data and “injected” into mod- els trained on native data (Rozovskaya and Roth, ...are ... See full document
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System Combination for Grammatical Error Correction
... of error-annotated learner (“par- allel”) ...correcting grammatical errors in ...Other approaches combine the advantages of classification and SMT (Dahlmeier and Ng, 2012a) and sometimes also include ... See full document
12
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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A Hybrid Model For Grammatical Error Correction
... Generally, for GEC on annotated data such as the NUCLE corpus (Dahlmeier et al., 2013) in this year’s shared task which contains both origi- nal errors and human annotations, there are two main types of ... See full document
8
Ground Truth for Grammatical Error Correction Metrics
... All metrics deviate from the human rankings, which may in part be because automatic metrics equally weight all error types, when some errors may be more tolerable to human judges than oth- ers. For example, ... See full document
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