[PDF] Top 20 Neural Sequence Labelling Models for Grammatical Error Correction
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Neural Sequence Labelling Models for Grammatical Error Correction
... One of the first approaches to GEC as an SMT task is the one by Brockett et al. (2006), who gen- erate artificial data based on hand-crafted rules to train a model that can correct countability er- rors. Dahlmeier and Ng ... See full document
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Fluency Boost Learning and Inference for Neural Grammatical Error Correction
... seq2seq error cor- rection model and error generation model are as follows: the encoder of the seq2seq models is a 2-layer bidirectional GRU RNN and the decoder is a 2-layer GRU RNN with the general ... See full document
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A Nested Attention Neural Hybrid Model for Grammatical Error Correction
... hybrid models and nested attention on the GEC ...language models for grammati- cal error correction is well known, and such mod- els have been used in classifier and MT-based sys- ...such ... See full document
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Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models
... ical error correction (GEC) that has not yet been adequately explored is adaptation based on the native language (L1) of writers, despite the marked influences of L1 on second lan- guage (L2) ...a ... See full document
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Approaching Neural Grammatical Error Correction as a Low Resource Machine Translation Task
... Previously, neural methods in grammatical er- ror correction (GEC) did not reach state-of- the-art results compared to phrase-based sta- tistical machine translation (SMT) ...between neural ... See full document
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The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction
... language models or had them as integral parts of their systems (Kao et ...as Neural Machine Translation- based approaches took over, but LMs remained an integral part of the GEC systems (Junczys- Dowmunt ... See full document
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Automatic Grammatical Error Correction for Sequence to sequence Text Generation: An Empirical Study
... One valid solution to this challenge is conduct- ing grammatical error correction (GEC) for ma- chine generated sentences. Recent GEC systems (Chollampatt and Ng, 2018; Junczys-Dowmunt et al., 2018; ... 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 ...and models can also benefit from the additional contextual information in correct- ing other ... 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 ... 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 ... See full document
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Neural Grammatical Error Correction Systems with Unsupervised Pre training on Synthetic Data
... round correction approach has been further ex- tended (Ge et ...two models display unique advantages for specific error types as they decode with different ... 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 ... See full document
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Neural and FST based approaches to grammatical error correction
... error correction. We present a system pipeline that utilises both error detection and correction ...complementary neural ma- chine translation systems: one using convo- lutional ... See full document
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Grammatical error correction using neural machine translation
... We propose a similar two-step approach: 1) align- ing the unknown words (i.e. UNK tokens) in the tar- get sentence to their origins in the source sentence with an unsupervised aligner; 2) building a word level ... See full document
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Grammatical Error Correction with Neural Reinforcement Learning
... predicted word sequence (ˆ y 1 t−1 ) at test time. Namely, the model is not exposed to the predicted words in training time. This is problematic, be- cause once the model fails to predict a correct word at test ... See full document
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Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
... Figure 1 hence shows how each team’s score for detection differed in relation to their score for correction. While CAMB scored highest for de- tection overall, it is interesting to note that CUUI ultimately ... See full document
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Evaluating LSTM models for grammatical function labelling
... Top-down tree LSTM Intuitively, it seems more natural to present the input as a tree struc- ture when trying to predict the dependency labels. We do that by adopting the top-down tree LSTM model (Zhang et al., 2016) that ... See full document
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A Tree Transducer Model for Grammatical Error Correction
... five error types: Article or determiner, preposition, noun number, verb form, and subject-verb agreement ...other error types are also included in the error ...other error types to the correct ... See full document
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A Beam Search Decoder for Grammatical Error Correction
... ceived comparatively less attention. Brockett et al. (2006) use an SMT system to correct errors in- volving mass noun errors. Because no large anno- tated learner corpus was available, the training data was created ... See full document
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Automatic Metric Validation for Grammatical Error Correction
... Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between hu- man and metric-induced rankings. How- ever, such correlation studies are costly, ... See full document
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