[PDF] Top 20 Language Model Based Grammatical Error Correction without Annotated Training Data
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Language Model Based Grammatical Error Correction without Annotated Training Data
... on grammatical error correction (GEC), re- search into language model (LM) based ap- proaches to GEC has largely ...minimal annotated data ( ∼ 1000 sentences), but ... See full document
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Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models
... predicting error distributions in ESL data (Berzak et al., 2015). L1-based adaptation has previously shown to improve GEC for specific error types using the classification ...L1-specific ... See full document
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Cross Sentence Grammatical Error Correction
... native language is English and contain at least two English sentences with a minimum of one ...the language of a sentence using ...select annotated English sentences (with at most 80 tokens) as our ... See full document
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An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
... Spelling Error (SSE) Lichtarge et ...pseudo data D p ...candidates based on the sum of the two scores. Sentence-level Error Detection (SED) SED classifies whether a given sentence contains a ... See full document
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A Nested Attention Neural Hybrid Model for Grammatical Error Correction
... hybrid model substan- tially improves upon the baseline, achieving a gain of close to 3 points on the test ...word/character model with a single level of atten- tion brings a large improvement as well, ... See full document
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Minimally Augmented Grammatical Error Correction
... cal error correction. We introduce Minimally- Augmented Grammatical Error Correction (MAGEC) that does not require any error- labelled ...is based on a simple but ... 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 ...tem. Training is performed on publicly avail- able data, along with artificial ... See full document
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Improving Grammatical Error Correction via Pre Training a Copy Augmented Architecture with Unlabeled Data
... (NMT) based systems were built for the GEC ...guage model and some of them used partially pre- trained parameters, which improve their results by ... 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
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Candidate re ranking for SMT based grammatical error correction
... LM: Language models are widely used in GEC, especially to rank correction suggestions proposed by other ...are based on the 5-gram ‘bing-body:apr10’ ... See full document
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Grammatical Error Correction: Machine Translation and Classifiers
... Training without supervision is possible in the classification framework, as ...native data that is assumed to be cor- rect; the classifier uses context words around each preposition as ...resulting ... See full document
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Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
... Having extracted the edits, the next step is to as- sign them error types. While Swanson and Ya- mangil (2012) did this by means of maximum entropy classifiers, one disadvantage of this ap- proach is that such ... See full document
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Grammatical Error Detection and Correction using a Single Maximum Entropy Model
... the grammatical error detection is regarded as a multi-label classification ...in training corpus is assigned a la- bel according to the golden ...rule based using an extend- ed version of ... See full document
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Grammatical Error Correction Using Feature Selection and Confidence Tuning
... learning based approaches collect their features empirical- ly and mainly depend on the feature selection of the classifiers which may bring about noises and increase the computational complexity when the feature ... See full document
5
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 ...source language to a grammatical target ...this data ... See full document
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The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction
... either based on language models or had them as integral parts of their systems (Kao et ...Translation- based approaches took over, but LMs remained an integral part of the GEC systems (Junczys- ... 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 ...left-to-right model with a right-to- left ...specific error types as they decode with different ...right-to-left model, but ... See full document
12
A Hybrid Model For Grammatical Error Correction
... the without ones (without errors) for ...for without type is totally ran- dom without loss of ...three error types (ArtOrDet, Prep and Nn) and try several ratios of with-without ... See full document
8
Erroneous data generation for Grammatical Error Correction
... In this work, we present a novel erroneous data generating method for training English GEC mod- els. Our experiments show that Transformer mod- els pretrained on generated corpus significantly outperform ... See full document
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A Tree Transducer Model for Grammatical Error Correction
... in grammatical error cor- rection (Ng et ...grammar correction model is based on a tree-to-string transducer that is specified by a set of rules that each rewrite a tree fragment to a ... See full document
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