Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2022
Abstract
Recently, a new training oaxe loss has proven effective to ameliorate the effect of multimodality for non-autoregressive translation (NAT), which removes the penalty of word order errors in the standard cross-entropy loss. Starting from the intuition that reordering generally occurs between phrases, we extend oaxe by only allowing reordering between ngram phrases and still requiring a strict match of word order within the phrases. Extensive experiments on NAT benchmarks across language pairs and data scales demonstrate the effectiveness and universality of our approach. Further analyses show that ngram noaxe indeed improves the translation of ngram phrases, and produces more fluent translation with a better modeling of sentence structure.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, 2022 October 12-17
First Page
5035
Last Page
5045
Publisher
Association for Computational Linguistics
City or Country
Gyeongju, Republic of Korea
Citation
DU, Cunxiao; TU, Zhaopeng; WANG, Longyue; and JIANG, Jing.
ngram-OAXE: Phrase-based order-agnostic cross entropy for non-autoregressive machine translation. (2022). Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, 2022 October 12-17. 5035-5045.
Available at: https://ink.library.smu.edu.sg/sis_research/7616
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/2022.coling-1.446/