I'm a PhD student at Stanford NLP, where I'm working with Dan Jurafsky. I was previously 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 (e.g., word embeddings, additive compositionality) and evaluation (i.e., how good is your NLP model really?). While at Google, I also worked on graph representation learning and information extraction.
Publications
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Utility is in the Eye of the User: A Critique of NLP Leaderboards.
Kawin Ethayarajh and Dan Jurafsky.
EMNLP 2020 (virtual).
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BLEU Neighbors: A Reference-less Approach to Automatic Evaluation.
Kawin Ethayarajh and Dorsa Sadigh.
EMNLP 2020 - Eval4NLP.
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Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds.
Kawin Ethayarajh.
ACL 2020 (virtual).
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How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.
Kawin Ethayarajh.
EMNLP 2019 (oral).
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Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space.
Kawin Ethayarajh.
EMNLP 2019 (poster).
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Understanding Undesirable Word Embedding Associations.
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst.
ACL 2019 (oral).
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Towards Understanding Linear Word Analogies.
Kawin Ethayarajh, David Duvenaud, and Graeme Hirst.
ACL 2019 (poster).
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Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline.
Kawin Ethayarajh.
ACL 2018 - Repl4NLP (best paper).
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A Rose by Any Other Name: Understanding Judicial Decisions that Do Not Cite Precedent.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.
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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