Grammatical and context-sensitive error correction using a statistical machine translation framework

2012 ◽  
Vol 43 (2) ◽  
pp. 187-206 ◽  
Author(s):  
Nava Ehsan ◽  
Heshaam Faili
Keyword(s):  
Error Correction ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Context Sensitive

Improve Statistical Machine Translation with Context-Sensitive Bilingual Semantic Embedding Model

2014 ◽  
Author(s):  
Haiyang Wu ◽  
Daxiang Dong ◽  
Xiaoguang Hu ◽  
Dianhai Yu ◽  
Wei He ◽  
...  
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Context Sensitive ◽  
Semantic Embedding

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

Factored Statistical Machine Translation for German-English

2018 ◽  
Vol 5 (1) ◽  
pp. 37-45
Author(s):  
Darryl Yunus Sulistyan
Keyword(s):  
Machine Translation ◽  
English Language ◽  
Statistical Machine Translation ◽  
New Model ◽  
Language Pair

Machine Translation is a machine that is going to automatically translate given sentences in a language to other particular language. This paper aims to test the effectiveness of a new model of machine translation which is factored machine translation. We compare the performance of the unfactored system as our baseline compared to the factored model in terms of BLEU score. We test the model in German-English language pair using Europarl corpus. The tools we are using is called MOSES. It is freely downloadable and use. We found, however, that the unfactored model scored over 24 in BLEU and outperforms the factored model which scored below 24 in BLEU for all cases. In terms of words being translated, however, all of factored models outperforms the unfactored model.

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