[PDF] Top 20 Grammatical error correction using neural machine translation
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Grammatical error correction using neural machine translation
... We use the publicly available FCE dataset (Yan- nakoudakis et al., 2011), which is a part of the Cambridge Learner Corpus (CLC) (Nicholls, 2003). The FCE dataset contains 1,244 scripts produced by learners taking the ... 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 ... See full document
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Near Human Level Performance in Grammatical Error Correction with Hybrid Machine Translation
... automated Grammatical Er- ror Correction (GEC): GEC based on Sta- tistical Machine Translation (SMT) and GEC based on Neural Machine Translation ... See full document
7
Neural Grammatical Error Correction with Finite State Transducers
... space using standard FST operations like composition, and then constrain the output of a neural GEC system to that ...space using word-level context-independent confusion sets (Bryant and Briscoe, ... See full document
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Improving Grammatical Error Correction via Pre Training a Copy Augmented Architecture with Unlabeled Data
... 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 of them used ... See full document
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A Nested Attention Neural Hybrid Model for Grammatical Error Correction
... in neural mod- els for GEC, to capture global fluency errors and local errors in spelling and closely related morpho- logical variants, while obtaining open vocabulary ...in neural machine ... See full document
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Connecting the Dots: Towards Human Level Grammatical Error Correction
... few error types, the CoNLL-2014 shared task dealt with correction of all kinds of textual ...2016). Neural machine translation ap- proaches have also showed some promise (Xie et ... See full document
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Corpora Generation for Grammatical Error Correction
... the Grammatical Error Correc- tion (GEC) task can be credited to approaching the problem as a translation task (Brockett et ...a grammatical target language. This has enabled Neural ... See full document
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Neural Sequence Labelling Models for Grammatical Error Correction
... re-ranking using a neural sequence- labelling model that calculates the probability of each token in a sentence being correct or incor- rect in ...sentence. Using a lin- ear combination of a small ... See full document
12
Neural and FST based approaches to grammatical error correction
... BPE is introduced to alleviate the rare-word problem, and rare and unknown words are split into multiple frequent subword tokens (Sennrich et al., 2016b). NMT systems often limit vocabu- lary size on both source and ... See full document
12
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
11
The AMU System in the CoNLL 2014 Shared Task: Grammatical Error Correction by Data Intensive and Feature Rich Statistical Machine Translation
... 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
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Grammatical Machine Translation
... minimum- error-rate training on the development set, and for translating the test set, we considered 1 German parse for each source sentence, 10 transferred f- structures for each source parse, and 1,000 gener- ... See full document
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System Combination for Grammatical Error Correction
... The parallel corpora that we use to train the translation model come from two different sources. The first corpus is NUCLE (Dahlmeier et al., 2013), containing essays written by students at the National University ... See full document
12
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
7
A Hybrid Model For Grammatical Error Correction
... A pre-processing and a post-processing filter are utilized which include filters for some idio- matic phrases extracted from the training dataset. The Frequent Pattern Growth Algorithm (FP- Growth) is widely used for ... See full document
8
Cross Sentence Grammatical Error Correction
... Automatic grammatical error correction (GEC) research has made remarkable progress in the past ...reliably using cross-sentence context and models can also benefit from the additional ... See full document
11
Grammatical Error Correction Using Integer Linear Programming
... Previous work in the machine learning approach typically formulates the task as a classication problem. Article and preposition errors are the two main research topics (Knight and Chander, 1994; Han et al., 2006; ... See full document
10
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 time, it ... See full document
7
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
... 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
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