Title
Everything Is All It Takes - A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction.
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.
Year
Venue
DocType
2021
EMNLP
Conference
Volume
Citations 
PageRank 
2021.emnlp-main
0
0.34
References 
Authors
0
13
Name
Order
Citations
PageRank
Mahsa Yarmohammadi100.34
Shijie Wu200.34
Marc Marone300.34
Haoran Xu400.34
Seth Ebner500.34
Guanghui Qin611.36
Chen Yunmo710.68
Jialiang Guo800.34
Craig Harman918511.41
Kenton Murray1000.34
Aaron Steven White11256.56
Mark Dredze123092176.22
Benjamin Van Durme13126892.32