[PDF] Top 20 Systematically Adapting Machine Translation for Grammatical Error Correction
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Systematically Adapting Machine Translation for Grammatical Error Correction
... scoring translation rules, it can work on any aligned text, and is sim- ilar to the forthcoming ERRANT toolkit, which is uses a rule-based framework for automatically cat- egorizes grammatical edits (Bryant ... See full document
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Improving Chinese Grammatical Error Correction with Corpus Augmentation and Hierarchical Phrase based Statistical Machine Translation
... Chinese grammatical error diagnosis (CGED). We use a statistical machine translation method already ap- plied to several similar tasks (Brockett et ...alternative translation models ... See full document
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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 systems ... See full document
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Near Human Level Performance in Grammatical Error Correction with Hybrid Machine Translation
... Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, and Alexandra Birch. 2018. ... See full document
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Approaching Neural Grammatical Error Correction as a Low Resource Machine Translation Task
... The NUCLE corpus matches the domain of the CoNLL benchmarks perfectly. It is however much smaller than the Lang-8 corpus. A setting like this seems to be a good fit for domain-adaptation techniques. Sennrich et al. ... 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
... To accommodate for parameter tuning, we mod- ify the standard 4-fold cross validation procedure. The test set in each of the four training/testing runs is again divided into two halves. The first half is treated as a ... See full document
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Phrase based Machine Translation is State of the Art for Automatic Grammatical Error Correction
... tions remain. We further divide the data into four folds. Each folds serves as development set for pa- rameter tuning, while the three remaining parts are treated as translation model training data. The full ... 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 ...specific error types using the classification ...the error fre- quency in L1-specific text to improve ...article correction, by ... See full document
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Grammatical Error Correction with Neural Reinforcement Learning
... To capture fluency as well as grammaticality in evaluation on such references, we use GLEU as the reward. We have shown GLEU to be more strongly preferred than other GEC metrics by na- tive speakers (Sakaguchi et al., ... See full document
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Ground Truth for Grammatical Error Correction Metrics
... tasks, grammatical error correction metrics must be evaluated against ground ...a grammatical correction, together with the fact that the use case for grammatically-corrected output is ... See full document
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Corpora Generation for Grammatical Error Correction
... CoNLL-2014 was reported by Junczys-Dowmunt et al. (2018) using an ensemble of neural Trans- former models (Vaswani et al., 2017), where the decoder side of each model is pretrained as a lan- guage model. From a modeling ... See full document
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Factored Statistical Machine Translation for Grammatical Error Correction
... phrase-based translation models, factored models make use of additional linguistic clues to guide the system such that it generates translated sentences in which morphological and syntactic constraints are met ... See full document
8
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 spe- ... See full document
7
Exploring Grammatical Error Correction with Not So Crummy Machine Translation
... grammar correction; they learn a noise model from a dataset of errorful sen- tences but do not rely on ...round-trip translation for such sentences via a single pivot language ...round-trip ... See full document
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Grammatical Error Correction: Machine Translation and Classifiers
... specific error type. Because an error type needs to be de- fined, typically only well-defined mistakes can be addressed in a straightforward ...an error type, a confusion set is specified and ... See full document
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Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation
... regarding grammatical error correction of essays written by English as a Second Language (ESL) ...errors, grammatical error correc- tion methods that use statistical machine ... See full document
6
Constrained Grammatical Error Correction using Statistical Machine Translation
... Following previous approaches, we decided to in- crease the size of our training set by introducing new sentences containing artificial errors. This has many potential advantages. First, it is an eco- nomic and efficient ... See full document
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Automatic Metric Validation for Grammatical Error Correction
... a correction which is identical to the output gets a score of less than 1, we experiment with an additional metric, MAX-SARI, which coincides with SARI for a sin- gle reference, and computes the maximum single- ... See full document
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Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
... new error scheme is based solely on au- tomatically obtained properties of the data, there are no gold standard labels against which to evalu- ate classifier ...predicted error types for 200 randomly chosen ... See full document
13
Erroneous data generation for Grammatical Error Correction
... In this section, we describe our error generating method. For each sentence, we assign a proba- bility distribution (as shown in Table 4) to deter- mine the number of errors according to the sen- tence length. The ... See full document
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