Giannis Karamanolakis
@gkaraml
Applied Scientist @Amazon AGI: working on ML/NLP/weakly-supervised learning. Previously @MSFTResearch, @Columbia, @behaviorsignals. Playing the bayess guitar.
ID: 2148960932
https://gkaramanolakis.github.io/ 22-10-2013 12:42:30
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A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios This survey is a great starting point for learning about low-resource NLP, common methods, and open challenges. Work by Jannik Strötgen Michael A. Hedderich Dietrich Klakow arxiv.org/abs/2010.12309
Excited about our new paper on estimating the causal effects of linguistic properties! E.g., does writing an email politely cause faster responses? To be presented at #NAACL2021 (arxiv.org/pdf/2010.12919…) W/ @rpryzant Dallas Card Victor Veitch 🔸 Dan Jurafsky 🧵(1/5)
highlight of my The Earth Institute post doc has been working with Columbia University students- celebrating meeting each other IRL for the first time yesterday after a hard virtual year! Congrats to Tejit/ Johanna/Dorothee on graduating ColumbiaCompSci thanks for helping me start my lab!
ASTRA: Self-training with Weak Supervision by Giannis Karamanolakis,Subhabrata Mukherjee et al. Combines rules, unlabeled data and limited labeled with self-supervision. Great results for text classification. 👩💻 github.com/microsoft/ASTRA 📝aclanthology.org/2021.naacl-mai… #python #nlproc #naacl21 #DataScience
I recently did a Q&A with ColumbiaCompSci talking about my research experience and projects in #ML and #NLP: cs.columbia.edu/2021/voices-of… Extra: you will also find a photo advertisement of my hometown in Greece :-)
Are you #PhDone (or close)? Would you like to live in the Washington, DC area and be part of the *exciting* Department of Computer Science - George Mason dept, working on Machine Translation and #nlproc for low-resource languages? I'm looking for a postdoc (ideally starting in 2022) -- do reach out if interested!
Congratulations Gianni's Karamanolakis Giannis Karamanolakis on successfully defending his PhD thesis "Efficient machine teaching frameworks for NLP" Defense slides are here: drive.google.com/file/d/1ez51lH…