Thanks very much to Deepset GmbH for hosting us, at The Place :):)
Talk 1: Transfer Learning in NLP - applied to Question Answering
Speakers: Branden Chan & Timo Möller
Abstract:
Since the transfer learning paradigm came to NLP, models have been able to convert learnings from massive amounts of unlabeled text data into performance gains on downstream tasks like document classification and NER. Another beneficiary of this revolution has been Question Answering, which has seen marked improvements since Google’s BERT model was released. In this talk, we will explain how to adjust a language model to answer questions in automated ways. Since new Language Model architectures are published on a monthly basis, an overview of current models will guide you on how to do state of the art NLP yourself.
Bios: Timo Möller is Co-Founder of the Machine Learning startup deepset. He studied Data Science in Maastricht and computational Neuroscience in Berlin, where he also worked several years as ML engineer. Branden Chan is a Stanford graduate in computational linguistics with experience as an NLP engineer - now working for deepset on bringing latest NLP techniques to the industry. Together they have trained large Language Models from scratch and are currently developing a customized Question Answering system for a DAX company.
