Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. Hover, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. ,  address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components,  achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system.
['Shamil Chollampatt', 'Kaveh Taghipour', 'Hwee Tou Ng']
I am writing a research paper on a idea Neural Network Translation Models for Grammatical Error Correction
[{"from": "human", "value": "I am writing a research paper on a idea Neural Network Translation Models for Grammatical Error Correction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. Hover, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. ,  address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components,  achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system."}]