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- W2987253016 abstract "The dominant approach to sequence generation is to produce a sequence in some predefined order, e.g. left to right. In contrast, we propose a more general model that can generate the output sequence by inserting tokens in any arbitrary order. Our model learns decoding order as a result of its training procedure. Our experiments show that this model is superior to fixed order models on a number of sequence generation tasks, such as Machine Translation, Image-to-LaTeX and Image Captioning." @default.
- W2987253016 created "2019-11-22" @default.
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- W2987253016 date "2019-11-01" @default.
- W2987253016 modified "2023-09-27" @default.
- W2987253016 title "Sequence Modeling with Unconstrained Generation Order" @default.
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