[PDF] Top 20 A Tree Transducer Model for Grammatical Error Correction
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A Tree Transducer Model for Grammatical Error Correction
... the correction task (Dahlmeier and Ng, ...the tree transducer, while for sentences that should be changed, 50% of hypothesis sets contained the correct ...oracle model increases sig- ... See full document
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Grammatical Error Correction with Alternating Structure Optimization
... an error and suggest the same correction. If the system’s correction differs from the human but is equally acceptable, it is considered (2) Both ...an error but fails to cor- rect it, we ... See full document
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A Beam Search Decoder for Grammatical Error Correction
... nel model for error ...Levy’s model does not allow the use of discrim- inative expert classifiers as our decoder does, but instead relies on a bigram language model to find grammatical ... See full document
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Grammatical Error Correction with Neural Reinforcement Learning
... We have presented a neural encoder-decoder model with reinforcement learning for GEC. To alleviate the MLE issues (exposure bias and token- level optimization), NRL learns the policy (model parameters) by ... See full document
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A Meta Learning Approach to Grammatical Error Correction
... In this paper, we present a novel approach to the GEC task using meta-learning. We focus mainly on article errors for two reasons. First, articles are one of the most significant sources of GE for the learners with ... 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, ...Learner error patterns+native predictions: ... See full document
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Erroneous data generation for Grammatical Error Correction
... method also introduces errors such as ramped → ramping. Our approach obtained competitive re- sults compared to the top systems in the BEA 2019 GEC Shared Task. Both our single model and en- semble models have ... See full document
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Grammatical Error Correction in Low Resource Scenarios
... When we inspected the results of a model trained on such dataset in Czech, we observed that the model often fails to correct casing errors and sometimes also errors in diacritics. Therefore, we extend ... 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
11
RACAI GEC – A hybrid approach to Grammatical Error Correction
... to model how likely the occurrence of an event is, given a his- tory of preceding ...small grammatical inad- vertences such as the local gender and case ... See full document
6
Noisy Channel for Low Resource Grammatical Error Correction
... and error type in Table ...of error types, but with varying quality. The model performs particularly well on spelling er- rors, subject–verb agreement errors and inserting missing ...the model ... See full document
6
The CUED’s Grammatical Error Correction Systems for BEA 2019
... Back-translation Back-translation (Sennrich et al., 2016b) has become the most widely used technique to use monolingual data in neural machine translation. Back-translation extends the existing parallel training set by ... See full document
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The BEA 2019 Shared Task on Grammatical Error Correction
... language model; LAIX, who augmented their transformer NMT model with a series of 8 error-type specific classifiers; and TMU, who mapped ‘cross-lingual’ word embeddings to the same space to induce a ... See full document
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Grammatical Error Correction Considering Multi word Expressions
... translation model and language model are im- proved by handling MWEs as single ...The correction performance of articles and prepositions that are likely to become a component word of MWEs is ... See full document
5
Language Model Based Grammatical Error Correction without Annotated Training Data
... the datasets with the ERRor ANnotation Toolkit (ERRANT) (Bryant et al., 2017) in an effort to standardise them. This standardisation is espe- cially important for JFLEG which is not explicitly annotated and so ... See full document
7
Joint Learning and Inference for Grammatical Error Correction
... a model that jointly learns each pair of these phenom- ...learning model exhibits superior performance, as it is also able to overcome the problem of the noisy context encountered by the individual mod- els ... See full document
12
Grammatical Error Detection and Correction using a Single Maximum Entropy Model
... task. Error correc- tion operations are encoded as a group of predefined labels and therefore the task is formulized as a multi-label classifica- tion ...entropy model is used for the classification ... See full document
9
A Nested Attention Neural Hybrid Model for Grammatical Error Correction
... neural model with two nested levels of attention: word-level and ...The model addresses the unique challenges of the grammatical error correction task and achieves the best reported ... See full document
10
Corpora Generation for Grammatical Error Correction
... Grammatical Error Correction (GEC) has been recently modeled using the sequence- to-sequence framework. However, unlike se- quence transduction problems such as ma- chine translation, GEC suffers ... See full document
11
Grammatical Error Correction as Multiclass Classification with Single Model
... language model is built by the SRILM toolkit (Stolcke and others, ...for grammatical error correction should be trained from corpus that it- self is grammatically correct, and the EuroParl ... See full document
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