Poster

Sequence Modeling with Unconstrained Generation Order

Dmitrii Emelianenko ⋅ Elena Voita ⋅ Pavel Serdyukov

Keywords: Generative Models Deep Learning Applications -> Natural Language Processing; Deep Learning; Deep Learning -> Attention Models; Optimization Stochastic Optim

2019 Poster

[ Paper] [ Poster]

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.

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