The Conference for Machine Learning Innovation

The Bigger the Better for NLP – or not?

Session
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Register until April15:
✓Save up to 310 €
✓ 2-in-1 conference special
✓10 % Team Discount
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Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until November 7th:
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✓10% Team Discount
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Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now

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.

Take me to the full program of Zum vollständigen Programm von Munich Munich .

This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

This Session belongs to the Diese Session gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Berlin Berlin .

This Session Diese Session belongs to the gehört zum Programm von MunichMunich program. Take me to the current program of . Hier geht es zum aktuellen Programm von Munich Munich , Singapore Singapore or oder Berlin Berlin .

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