Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce referential translation machines (RTM) for quality estimation of translation outputs. RTMs are a computational model for identifying the translation acts beten any two data sets with respect to a reference corpus selected in the same domain, which can be used for estimating the quality of translation outputs, judging the semantic similarity beten text, and evaluating the quality of student ansrs. RTMs achieve top performance in automatic, accurate, and language independent prediction of sentence-level and word-level statistical machine translation (SMT) quality. RTMs remove the need to access any SMT system specific information or prior knowledge of the training data or models used when generating the translations.  develop novel techniques for solving all subtasks in the WMT13 quality estimation (QE) task (QET 2013) based on individual RTM models. Our results achieve improvements over last year’s QE task results (QET 2012), as ll as our previous results, provide new features and techniques for QE, and rank 1st or 2nd in all of the subtasks.
['Ergun Bicici']
I am writing a research paper on a idea Referential Translation Machines for Quality Estimation
[{"from": "human", "value": "I am writing a research paper on a idea Referential Translation Machines for Quality Estimation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce referential translation machines (RTM) for quality estimation of translation outputs. RTMs are a computational model for identifying the translation acts beten any two data sets with respect to a reference corpus selected in the same domain, which can be used for estimating the quality of translation outputs, judging the semantic similarity beten text, and evaluating the quality of student ansrs. RTMs achieve top performance in automatic, accurate, and language independent prediction of sentence-level and word-level statistical machine translation (SMT) quality. RTMs remove the need to access any SMT system specific information or prior knowledge of the training data or models used when generating the translations.  develop novel techniques for solving all subtasks in the WMT13 quality estimation (QE) task (QET 2013) based on individual RTM models. Our results achieve improvements over last year\u2019s QE task results (QET 2012), as ll as our previous results, provide new features and techniques for QE, and rank 1st or 2nd in all of the subtasks."}]