scholarly journals The AMU System in the CoNLL-2014 Shared Task: Grammatical Error Correction by Data-Intensive and Feature-Rich Statistical Machine Translation

2014 ◽  
Author(s):  
Marcin Junczys-Dowmunt ◽  
Roman Grundkiewicz
Keyword(s):  
Error Correction ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Shared Task ◽  
Data Intensive ◽  
Grammatical Error

A Comprehensive Survey of Grammatical Error Correction

2021 ◽  
Vol 12 (5) ◽  
pp. 1-51
Author(s):  
Yu Wang ◽  
Yuelin Wang ◽  
Kai Dang ◽  
Jie Liu ◽  
Zhuo Liu
Keyword(s):  
Error Correction ◽  
Machine Translation ◽  
Language Processing ◽  
Data Augmentation ◽  
Intelligent System ◽  
Statistical Machine Translation ◽  
Error Type ◽  
Data Annotation ◽  
Depth Analysis ◽  
Grammatical Error

Grammatical error correction (GEC) is an important application aspect of natural language processing techniques, and GEC system is a kind of very important intelligent system that has long been explored both in academic and industrial communities. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning. However, there is not a survey that untangles the large amount of research works and progress in this field. We present the first survey in GEC for a comprehensive retrospective of the literature in this area. We first give the definition of GEC task and introduce the public datasets and data annotation schema. After that, we discuss six kinds of basic approaches, six commonly applied performance boosting techniques for GEC systems, and three data augmentation methods. Since GEC is typically viewed as a sister task of Machine Translation (MT), we put more emphasis on the statistical machine translation (SMT)-based approaches and neural machine translation (NMT)-based approaches for the sake of their importance. Similarly, some performance-boosting techniques are adapted from MT and are successfully combined with GEC systems for enhancement on the final performance. More importantly, after the introduction of the evaluation in GEC, we make an in-depth analysis based on empirical results in aspects of GEC approaches and GEC systems for a clearer pattern of progress in GEC, where error type analysis and system recapitulation are clearly presented. Finally, we discuss five prospective directions for future GEC researches.

Factored Statistical Machine Translation for Grammatical Error Correction

2014 ◽  
Author(s):  
Yiming Wang ◽  
Longyue Wang ◽  
Xiaodong Zeng ◽  
Derek F. Wong ◽  
Lidia S. Chao ◽  
...  
Keyword(s):  
Error Correction ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Grammatical Error

scholarly journals MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation

2019 ◽  
Vol 28 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Sainik Kumar Mahata ◽  
Dipankar Das ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Target Language ◽  
Data Sets ◽  
Shared Task ◽  
Automatic Translation ◽  
External Data ◽  
Statistical Mt

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.

scholarly journals Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

2018 ◽  
Author(s):  
Marcin Junczys-Dowmunt ◽  
Roman Grundkiewicz ◽  
Shubha Guha ◽  
Kenneth Heafield
Keyword(s):  
Error Correction ◽  
Machine Translation ◽  
Low Resource ◽  
Grammatical Error
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