From the Makers of Haystack: Ask your Data with Neural Search (in German too!)

Call us crazy but we decided to organise another great line-up despite the fact that our recently announced Meetup has not even happened yet! Let's explore an area where natural language processing and search meet -- automatic Question-Answering. Plenty of recent advances but primarily for languages such as English. Now I am sure you are as excited as us to learn more about the recently published German QA dataset and many other things ...

Come and join us (again)!
Udo, David & Bernd

--------------

Speakers:
Branden Chan (NLP Engineer at deepset)
Timo Möller (Co-founder and Head of Machine Learning at deepset)

Title:
Ask your Data with Neural Search (Now also in German!)

Abstract:
Search is ubiquitous. It’s expected to be packaged with just about any program that interacts with text. With breakthroughs in Machine Learning (ML) and Natural Language Processing (NLP), search technology has taken a big leap forward and we at deepset are committed to tracking the latest trends. Our open source framework, Haystack, is a collection of NLP pipelines which can sift through millions of documents, answer full sentence questions and summarize documents. With the release of our new German Question Answering and Passage Retrieval datasets (https://deepset.ai/germanquad), Neural Search is now also available in the German language. In this talk, we will be discussing what is possible today in the realm of Neural Search, and offer first hand advice about how to make it work for your language and your domain.

Short Bios:
Branden Chan is an NLP Engineer at deepset, keeping one finger of the pulse of NLP research and applying cutting edge models to real-world problems. He holds masters degrees in Historical Linguistics (University of Cambridge) and Computational Linguistics (Stanford University) and is an active maintainer of the Haystack Open Source search framework.

Timo Möller co-founded deepset - as Head of Machine Learning he is at the intersection of latest tech and production use. He studied computational neuroscience, is an open-source fan and passionate NLP engineer. Currently he implements new search technologies like generative Question Answering, researches model speed, confidence or out-of-domain performance during industry integrations of deepset's open-source framework Haystack.