To Annotate or Not? Predicting Performance Drop under Domain Shift

Hady Elsahar, Matthias Galle
NAVER LABS Europe
EMNLP 2019

In this paper, we propose a method that can predict the drop inaccuracy of a model suffering domain-shift with an error rate as little as 2.15% for sentiment analysis and 0.89% for POS tagging
respectively, without needing any labelled examples from the target domain.

[Code] — [Paper] —[Blogpost]