Is Linguistics Missing from NLP Research? with Emily Bender
EPISODE 376
|
MAY
18,
2020
Watch
Follow
Share
About this Episode
Today we're joined by Emily M. Bender, Professor of Linguistics at the University of Washington.
Our discussion covers a lot of ground, but centers on the question, "Is Linguistics Missing from NLP Research?" We explore if we would be making more progress, on more solid foundations, if more linguists were involved in NLP research, or is the progress we're making (e.g. with deep learning models like Transformers) just fine?
Later this afternoon (3pm PT) we'll be hosting a viewing party with Emily over on our YouTube channel. Sam and Emily will be in the live chat answering your questions from the conversation. Register at twimlai.com/376viewing!
About the Guest
Emily Bender
University of Washington
Resources
- BenderRule: On Naming the Languages We Study and Why It Matters
- Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
- A Typology of Risks of Voice Technology
- Towards Guidelines for Evaluating NLP Shared Tasks
- A Linguistic Lens on Artificial Intelligence
- BERT-ology
- SuperGLUE Benchmark - Paper
- #229 - Pathologies of Neural Models and Interpretability with Alvin Grissom II
- Radical AI Podcast - Love, Challenge, and Hope: Building a Movement to Dismantle the New Jim Code with Ruha Benjamin
- Paper: Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science
- Paper: Model Cards for Model Reporting
- #88 - Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru - Paper
- Data Nutrition Project
- Paper: Experience Grounds Language
- Paper: Probing Neural Network Comprehension of Natural Language Arguments
- Video: Emily M. Bender – A Typology of Ethical Risks in Language Technology with an Eye Towards Where Transparent Documentation Can Help
- Learning Meaning in Natural Language Processing — The Semantics Mega-Thread
- NLP/CL Twitter Megathread
- Paper: Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
