Annotator Bias

The modern deep learning approaches to natural language processing are voracious in their demands for large corpora to train on.  Folk wisdom estimates used to be around 100k documents were required for effective training.  The availability of broadly trained, general-purpose models like BERT has made it possible to do transfer learning to achieve novel results on much smaller corpora.

Thanks to these advancements, an NLP researcher might get value out of fewer examples since they can use the transfer learning to get a head start and focus on learning the nuances of the language specifically relevant to the task at hand.  Thus, small specialized corpora are both useful and practical to create.

In this episode, Kyle speaks with [Mor Geva](https://mega002.github.io/), lead author on the recent paper [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://arxiv.org/abs/1908.07898), which explores some unintended consequences of the typical procedure followed for generating corpora.

Source code for the paper available here: [https://github.com/mega002/annotator_bias](https://github.com/mega002/annotator_bias)

Guest

Mor Geva: Mor Geva is a researcher at the Allen Institute for AI (AI2), working in the field of Natural Language Processing. Her research focuses on developing systems that can reason over text in a robust and interpretable manner. She completed her Ph.D. in Computer Science and B.Sc. in Bioinformatics at Tel Aviv University. During her Ph.D., Mor interned at AI2, Google AI, and Microsoft Media AI. She was awarded the Dan David prize for graduate students in the field of AI, was nominated as one of the MIT Rising Stars in EECS, and is a laureate of the Séphora Berrebi scholarship in Computer Science.