Context-Interactive Pre-Training for Document Machine Translation
Pengcheng Yang, Pei Zhang, Boxing Chen, Jun Xie, Weihua Luo
Abstract
Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information. However, it typically suffers from a lack of doc-level bilingual data. To remedy this, here we propose a simple yet effective context-interactive pre-training approach, which targets benefiting from external large-scale corpora. The proposed model performs inter sentence generation to capture the cross-sentence dependency within the target document, and cross sentence translation to make better use of valuable contextual information. Comprehensive experiments illustrate that our approach can achieve state-of-the-art performance on three benchmark datasets, which significantly outperforms a variety of baselines.
BibTeX
@inproceedings{yang-etal-2021-context,
title = "Context-Interactive Pre-Training for Document Machine Translation",
author = "Yang, Pengcheng and
Zhang, Pei and
Chen, Boxing and
Xie, Jun and
Luo, Weihua",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.281/",
doi = "10.18653/v1/2021.naacl-main.281",
pages = "3589--3595"
}