Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical machine translation, and  investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models,  note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and  explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic back-translation,  can treat it as additional parallel training data, and  obtain substantial improvements on the WMT 15 task English German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results.  also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English->German.
['Rico Sennrich', 'Barry Haddow', 'Alexandra Birch']
I am writing a research paper on a idea Improving Neural Machine Translation Models with Monolingual Data
[{"from": "human", "value": "I am writing a research paper on a idea Improving Neural Machine Translation Models with Monolingual Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical machine translation, and  investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models,  note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and  explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic back-translation,  can treat it as additional parallel training data, and  obtain substantial improvements on the WMT 15 task English German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results.  also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English->German."}]