Sequence-to-Lattice Models for Fast Translation

Yuntian Deng, Alexander Rush


Abstract
Non-autoregressive machine translation (NAT) approaches enable fast generation by utilizing parallelizable generative processes. The remaining bottleneck in these models is their decoder layers; unfortunately unlike in autoregressive models (Kasai et al., 2020), removing decoder layers from NAT models significantly degrades accuracy. This work proposes a sequence-to-lattice model that replaces the decoder with a search lattice. Our approach first constructs a candidate lattice using efficient lookup operations, generates lattice scores from a deep encoder, and finally finds the best path using dynamic programming. Experiments on three machine translation datasets show that our method is faster than past non-autoregressive generation approaches, and more accurate than naively reducing the number of decoder layers.
Anthology ID:
2021.findings-emnlp.318
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3765–3772
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.318
DOI:
10.18653/v1/2021.findings-emnlp.318
Bibkey:
Cite (ACL):
Yuntian Deng and Alexander Rush. 2021. Sequence-to-Lattice Models for Fast Translation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3765–3772, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Sequence-to-Lattice Models for Fast Translation (Deng & Rush, Findings 2021)
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PDF:
https://aclanthology.org/2021.findings-emnlp.318.pdf