Neutralizing Subjectivity Bias with HuggingFace Transformers


The NLP task of text style transfer (TST) aims to automatically control the style attributes of a piece of text while preserving the content, which is an important consideration for making NLP more user-centric. In this session, we will explore text style transfer through an applied use case — neutralizing subjectivity bias in free text. Along the way, we’ll describe our sequence-to-sequence modeling approach leveraging HuggingFace Transformers, and present a set of custom, reference-free evaluation metrics for quantifying model performance. Finally, we’ll conclude with a discussion of ethics centered around our Applied Machine Learning Prototype: Exploring Intelligent Writing Assistance.


Andrew is a Research Engineer at Cloudera Fast Forward Labs where he spends his time researching the latest advances in the field of machine learning and building prototypes applied to real-world use cases. Prior to joining Cloudera, Andrew worked as a Data Scientist in Deloitte’s Analytics & Cognitive practice developing data products and delivering insights for Government and Public Sector organizations. Andrew holds a Bachelor’s Degree in Mechanical Engineering from Virginia Tech.

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