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Non-autoregressive Transformer by Position Learning

2019-11-25 03:08:42
Yu Bao, Hao Zhou, Jiangtao Feng, Mingxuan Wang, Shujian Huang, Jiajun Chen, Lei LI
arXiv_AI
       
Abstract

Non-autoregressive models are promising on various text generation tasks. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling is an essential problem in non-autoregressive text generation. In this study, we propose PNAT, which incorporates positions as a latent variable into the text generative process. Experimental results show that PNAT achieves top results on machine translation and paraphrase generation tasks, outperforming several strong baselines.

Abstract (translated)

URL

https://arxiv.org/abs/1911.10677

PDF

https://arxiv.org/pdf/1911.10677.pdf

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