Annotator Bias

Data Skeptic · Advanced ·🧬 Deep Learning ·6y ago

Key Takeaways

The video discusses annotator bias in natural language processing, specifically in the context of transfer learning and the use of pre-trained models like BERT, and explores the unintended consequences of typical corpus generation procedures.

Original Description

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, lead author on the recent paper Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets, 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
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The video discusses annotator bias in NLP and its implications for dataset creation and model training. It highlights the importance of considering annotator bias when generating corpora and using transfer learning. The paper's findings and source code are also discussed.

Key Takeaways
  1. Understand the concept of annotator bias in NLP
  2. Learn about the typical procedure for generating corpora
  3. Explore the unintended consequences of this procedure
  4. Consider the implications of annotator bias for model training and dataset creation
💡 Annotator bias can have significant implications for NLP model training and dataset creation, and researchers should consider this bias when generating corpora.

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