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Neural Machine Translation Using Multiple Back-translation Generated by Sampling
2020
Transactions of the Japanese society for artificial intelligence
A large-scale parallel corpus is indispensable to train encoder-decoder neural machine translation. The method of using synthetic parallel texts, called back-translation, in which target monolingual sentences are automatically translated into the source language, has been proven effective in improving the decoder. However, it does not necessarily help enhance the encoder. In this paper, we propose a method that enhances not only the decoder but also the encoder using target monolingual corpora
doi:10.1527/tjsai.a-ja9
fatcat:3etyetphhvbxreckxnazfvmdrq