The Conference for Machine Learning Innovation

The Bigger the Better for NLP – or not?

Session
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Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos

We have seen a whole pile of language models getting published in recent years, achieving better and better results for all kinds of standardized natural language processing (NLP) tasks. "Which transformer model from huggingface shall we use?" – This seems to be the only relevant question in the early phases of an NLP project.

I am exaggerating of course. Nevertheless, the language model enthusiasm tends to overshadow valid and sometimes even better solutions to NLP problems. You don’t need the power of BERT and co. for every project. And sometimes the drawbacks (such as memory consumption, runtime, etc.) even outweigh the benefits.

In this talk I make the case for a more diverse toolbox to approach NLP problems. I will illustrate my point with specific projects where we did and didn’t choose the right approach from the beginning and tell you what I learned.

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session Diese Session originates from the archive of stammt aus dem Archiv von MunichMunich . Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks