I'll be joining Stanford NLP in the fall as a PhD student. Until recently, I was a BMO National Scholar and John H. Moss Scholar at the University of Toronto, where I worked with Graeme Hirst and David Duvenaud.
My research interests are in representation learning for NLP (e.g., word embeddings, additive compositionality) and ML fairness. While at Google, I also worked on graph representation learning and information extraction.
Updates
- (09/28/2019) Two papers accepted to EMNLP 2019! Preprints forthcoming.
Publications
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How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.
Kawin Ethayarajh.
EMNLP 2019 (talk).
[paper] -
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space.
Kawin Ethayarajh.
EMNLP 2019 (poster).
[paper] -
Understanding Undesirable Word Embedding Associations.
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst.
ACL 2019 (talk).
[paper][blog][slides][bib] -
Towards Understanding Linear Word Analogies.
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst.
ACL 2019 (poster).
[paper][blog][bib] -
Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline.
Kawin Ethayarajh.
ACL 2018 - Repl4NLP (talk; best paper).
[paper][code][poster][slides][screencast][bib] -
A Rose by Any Other Name: Understanding Judicial Decisions that Do Not Cite Precedent.
Kawin Ethayarajh, Andrew Green, and Albert Yoon.
Journal of Empirical Legal Studies. 15(3): 563-596.
[paper] -
The Effect of Photoperiod on the Mood of Reddit Users.
Kawin Ethayarajh and Frank Rudzicz.
Cyberpsychology, Behavior, and Social Networking. 20(4): 238-245.
[paper][press]