1. Information Extraction: Key Learnings from Real NLP Projects (Jona Welsch)

Information Extraction and Natural Language Processing (NLP) have a large variety of business applications, ranging from the digitalisation of invoices or purchase orders to the automatic interpretation of free-form text. This talk is about key learnings and insights from projects for industry clients and public institutions, which were successfully brought into production. Learn about the pitfalls when processing PDF-Documents or where to effectively use Graph Neural Networks.

2. Boosting Search with Latest NLP – Transformers, Dense Vector Similarity and More (Timo Möller)

Nowadays almost every English Google search result is powered by a Transformer – a Language Model that is hard to scale in production. In this talk, we will dive into some of these modern search methods, show how to improve document retrieval via dense encoders, return exact answers via Question Answering and scale those pipelines to production workloads. Everything will be illustrated with code from the open-source framework Haystack.

3. Deep Learning for Natural Language Processing in Real Life Product (Chongko Snitwong Na Aryuttaya)

Implementing data science to product is hard. Why? We only know machine learning is cool. Everyone wants to have it but when data scientists try to fit machine learning to the business product, business people just wants proof of concept. At the end of the day, the results would be only on .csv file, nobody touches anymore. That’s why it is essential to know data product how it starts, how it ends. As a data science product manager, these things can combined!