Abstract

The EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing (DeepLo) takes place on Sunday, November 3rd, in Hong Kong, China, immediately before the main conference.
Natural Language Processing is being revolutionized by deep learning with neural networks. However, deep learning requires large amounts of annotated data, and its advantage over traditional statistical methods typically diminishes when such data is not available; for example, SMT continues to outperform NMT in many bilingually resource-poor scenarios. Large amounts of annotated data do not exist for many low-resource languages, and for high-resource languages it can be difficult to find linguistically annotated data of sufficient size and quality to allow neural methods to excel. Our workshop aimed to bring together researchers from the NLP and ML communities who work on learning with neural methods when there is not enough data for those methods to succeed out-of-the-box. Techniques of interest include self-training, paired training, distant supervision, semi-supervised and transfer learning, and human-in-the-loop algorithms such as active learning.

Date

2019

Authors

Colin Cherry, Greg Durrett, George Foster, Gholamreza Haffari, Shahram Khadivi, Nanyun Peng, Xiang Ren, Swabha Swayamdipta

Conference

Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)