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- W2890789346 abstract "We propose a novel model for Neural Machine Translation (NMT). Different from the conventional method, our model can predict the future text length and words at each decoding time step so that the generation can be helped with the information from the future prediction. With such information, the model does not stop generation without having translated enough content. Experimental results demonstrate that our model can significantly outperform the baseline models. Besides, our analysis reflects that our model is effective in the prediction of the length and words of the untranslated content." @default.
- W2890789346 created "2018-09-27" @default.
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- W2890789346 date "2018-09-02" @default.
- W2890789346 modified "2023-10-18" @default.
- W2890789346 title "Future-Prediction-Based Model for Neural Machine Translation" @default.
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- W2890789346 doi "https://doi.org/10.48550/arxiv.1809.00336" @default.
- W2890789346 hasPublicationYear "2018" @default.
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