FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, Eduard Hovy


Abstract
Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length.
Anthology ID:
D19-1437
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4282–4292
Language:
URL:
https://aclanthology.org/D19-1437
DOI:
10.18653/v1/D19-1437
Bibkey:
Cite (ACL):
Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, and Eduard Hovy. 2019. FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4282–4292, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow (Ma et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1437.pdf
Attachment:
 D19-1437.Attachment.zip
Code
 XuezheMax/flowseq +  additional community code
Data
WMT 2014WMT 2016WMT 2016 News