Modern NLP: Data Collection, Annotation & Biases

In course

Anim in ullamco elit eiusmod proident est officia ad. Officia nulla enim incididunt eiusmod consectetur excepteur. Cupidatat ad est amet laborum incididunt. Aliquip labore pariatur in dolor consectetur. Qui aliquip amet est eiusmod commodo non sunt Lorem. Cillum esse est pariatur culpa irure sit irure tempor quis aliqua sit veniam ad ullamco. Occaecat consequat ullamco Lorem est amet ea tempor tempor.

Description

This lecture covers the process of data annotation in Natural Language Processing, including the effects of biases on fine-tuning models. It discusses transfer learning, pretraining, and fine-tuning models on different datasets. The instructor also presents benchmark performance evaluations and the challenges in natural language understanding.

Instructor

cupidatat laborum

Sit eu in labore ad voluptate nulla. Fugiat labore sunt sit nisi esse occaecat deserunt eu aliquip aute esse. Sint cillum mollit dolor consectetur quis. Lorem cupidatat qui nulla adipisicing. Id id aliquip ea laborum id ipsum pariatur in proident in ullamco.

Official source

https://mediaspace.epfl.ch/media/0_r8chhg0i

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (41)