Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
3-2024
Abstract
In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer (MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.
Discipline
Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
EACL 2024: 18th Conference of the European Chapter of the Association for Computational Linguistics, Findings: St Julian's, Malta, March 17-22
First Page
582
Last Page
588
ISBN
9798891760936
Publisher
Association for Computational Linguistics (ACL)
City or Country
St. Julian's
Citation
DU, Cunxiao; ZHOU, Hao; TU, Zhaopeng; and JIANG, Jing.
Revisiting the Markov Property for machine translation. (2024). EACL 2024: 18th Conference of the European Chapter of the Association for Computational Linguistics, Findings: St Julian's, Malta, March 17-22. 582-588.
Available at: https://ink.library.smu.edu.sg/sis_research/8725
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://aclanthology.org/2024.findings-eacl.40