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- W3118166801 abstract "Attention-based encoder-decoder models have achieved great success in neural machine translation tasks. However, the lengths of the target sequences are not explicitly predicted in these models. This work proposes length prediction as an auxiliary task and set up a sub-network to obtain the length information from the encoder. Experimental results show that the length prediction sub-network brings improvements over the strong baseline system and that the predicted length can be used as an alternative to length normalization during decoding." @default.
- W3118166801 created "2021-01-05" @default.
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- W3118166801 date "2020-12-01" @default.
- W3118166801 modified "2023-09-23" @default.
- W3118166801 title "Predicting and Using Target Length in Neural Machine Translation." @default.
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