[PDF] Top 20 Approaching Neural Grammatical Error Correction as a Low Resource Machine Translation Task
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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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Grammatical Error Correction in Low Resource Scenarios
... in grammatical error correction (GEC) in ...shared task (Ng et ...single error-type classi- fiers and their combinations were due to larger amount of data surpassed by statistical and ... See full document
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The AMU System in the CoNLL 2014 Shared Task: Grammatical Error Correction by Data Intensive and Feature Rich Statistical Machine Translation
... tures. We suspect that the task-specific features allow the decoder to better exploit the potential of the Lang-8 data. This is verified by training NU- CLE+CCLM+LD+SF which scores only 25.82%. To support our ... See full document
9
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 ...Statistical Machine Translation ... See full document
12
Improving Grammatical Error Correction via Pre Training a Copy Augmented Architecture with Unlabeled Data
... 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 ... See full document
10
Neural and FST based approaches to grammatical error correction
... shared task on grammat- ical error ...both error detection and correction ...complementary neural ma- chine translation systems: one using convo- lutional networks and ... See full document
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Phrase based Machine Translation is State of the Art for Automatic Grammatical Error Correction
... for translation-focused settings: usually they consist of between 2000 and 3000 sentences, they should be a good representation of the testing data, sparse features require more sentences or more references, ...a ... 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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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 ...building machine learning classifiers for ... See full document
7
The BEA 2019 Shared Task on Grammatical Error Correction
... shared task on grammati- cal error correction five years ...based neural machine translation proved effective, and teams generally scored significantly higher in BEA-2019 than in ... 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 time, it ... See full document
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The CoNLL 2013 Shared Task on Grammatical Error Correction
... shared task, and Table 6 summarizes these ...shared task and in grammatical error correction research in general is to build a classifier for each error ...an error type ... See full document
12
The CoNLL 2014 Shared Task on Grammatical Error Correction
... shared task is the NUCLE corpus, the NUS Corpus of Learner English (Dahlmeier et ...matical error correction, since it prevents com- parative evaluations on a common benchmark test data ...The ... See full document
14
Improving Chinese Grammatical Error Correction with Corpus Augmentation and Hierarchical Phrase based Statistical Machine Translation
... The system is clearly optimized to achieve the best performance in terms of FP rate and accura- cy. However, this is because, as experiments showed, the system produces nearly all negative predictions, which causes ... See full document
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Data Augmentation for Low Resource Neural Machine Translation
... As NMT system we use a 4-layer attention- based encoder-decoder model as described in (Lu- ong et al., 2015) trained with hidden dimension 1000, batch size 80 for 20 epochs. In all experi- ments the NMT vocabulary is ... See full document
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Adaptive Knowledge Sharing in Multi Task Learning: Improving Low Resource Neural Machine Translation
... Neural Machine Translation (NMT) is no- torious for its need for large amounts of bilingual ...Multi- Task Learning (MTL) to leverage differ- ent linguistic resources as a source of inductive ... See full document
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NAIST at 2013 CoNLL Grammatical Error Correction Shared Task
... This paper describes the Nara Institute of Science and Technology (NAIST) er- ror correction system in the CoNLL 2013 Shared Task. We constructed three sys- tems: a system based on the Treelet Lan- guage ... See full document
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Naive Regularizers for Low-Resource Neural Machine Translation
... Neural machine translation models have little inductive bias, which can be a disad- vantage in low-resource ...the translation quality across mul- tiple ... See full document
10
Transfer Learning for Low Resource Neural Machine Translation
... the neural encoder-decoder framework for MT (Neco and Forcada, 1997; Casta˜no and Casacu- berta, 1997; Sutskever et ...recurrent neural network (encoder) to convert a source sen- tence into a dense, ... See full document
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NICT’s participation to WAT 2019: Multilingualism and Multi step Fine Tuning for Low Resource NMT
... text using Mecab 5 . Note that the implementation we used for our experiments learns and performs sub-word segmentation on the tokenized text. In order to compute BLEU we unsub-worded and detokenized Russian translations ... See full document
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