Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction

Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme


Abstract
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of “train on English, run on any language”, we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.
Anthology ID:
2021.emnlp-main.149
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1950–1967
Language:
URL:
https://aclanthology.org/2021.emnlp-main.149
DOI:
10.18653/v1/2021.emnlp-main.149
Bibkey:
Cite (ACL):
Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, and Benjamin Van Durme. 2021. Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1950–1967, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (Yarmohammadi et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.149.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.149.mp4
Code
 shijie-wu/crosslingual-nlp +  additional community code