Transfer Learning in NLP & Text Generation for Fake News Detection

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.

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Talk 2: Grover vs the fake news robot uprising
Speaker: Camille Van Hoffelen

Abstract: The debate about the dangers of language generation exploded with OpenAI's refusal to release the full GPT-2 model earlier this year. The conversation is still in full swing today about the best ways to defend ourselves against malicious applications of the technology. A key component of this debate is the ability to automatically differentiate generated language from human language. In this talk, we will first explore state-of-the-art text generation methods using language models. Then, we will breakdown generated text detection techniques, and introduce Grover: our potential saviour from the fake news apocalypse.

Bio: Camille is a Research Engineer at Seal Software, where he has spent 5 years building the leading enterprise software in contract analytics. He graduated with an MSci in Physics from Imperial College London. Nowadays, he focuses on implementing state-of-the-art NLP methods for legal AI, with experience in ML infrastructure and software development. He is driven by the impact of cutting-edge AI technologies, and strives to put these big ideas to use in the real world.