Today we’re joined by Huiji Gao, a Senior Engineering Manager of Machine Learning and AI at LinkedIn.
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In our conversation with Huiji, we dig into his interest in building NLP tools and systems, including a recent open-source project called DeText, a framework for generating models for ranking classification and language generation. We explore the motivation behind DeText, the landscape at LinkedIn before and after it was put into use broadly, and the various contexts it’s being used in at the company. We also discuss the relationship between BERT and DeText via LiBERT, a version of BERT that is trained and calibrated on LinkedIn data, the practical use of these tools from an engineering perspective, the approach they’ve taken to optimization, and much more!
Thanks to our Sponsor!
Thank you to our friends at LinkedIn for their continued support and for sponsoring today’s show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of every product the company builds for its members and customers. LinkedIn’s highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.
Connect with Huiji!
- Blog: DeText: A deep NLP framework for intelligent text understanding
- Paper: DeText: A Deep Text Ranking Framework with BERT
- Video: LinkedIn DeText: A Deep NLP Framework for Intelligent Text Understanding