John Hewitt
Hello! I’m a PhD student in computer science, conducting research in natural language processing at Stanford University.
I design and analyze machine learning models of human languages. I tend to think we can figure out what deep neural networks learn about language, and use these insights to design models that learn from less data. As an undergraduate at Penn, I worked in the lab of Chris Callison-Burch.
I’m particularly interested in pursuing advances that permit the use of NLP across all languages, all domains, and all use cases – to increase access to NLP technologies.
Feel free to look me up on Google Scholar or Twitter, or take my CV.
News
- [Fall 2018] I’ve started offering office hours for research-interested Stanford undergraduates!
- [Fall 2018] I’ve started my first year as a PhD student at Stanford!
Research Office Hours for Undergraduates (ROHU)
An open time for undergraduates looking for advice and discussions on natural language processing research. learn more
- When Wednesdays, 6:00-7:00 PM, starting 2018-10-03
- Where Gates Building, 2nd floor, A-wing big middle room
- Who You, Stanford undergraduates wanting to chat about research; me, wanting to help
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Publications
Publications
2018
A Distributional and Orthographic Aggregation Model for English Derivational Morphology
Daniel Deutsch*, John Hewitt* and Dan Roth.
ACL 2018. (to appear). (pdf)
*: Equal contribution; authors listed alphabeticallyLearning Translations via Images with a Massively Multilingual Image Dataset.
John Hewitt*, Daphne Ippolito*, Brendan Callahan, Reno Kriz, Derry Tanti Wijaya and Chris Callison-Burch.
ACL 2018. (to appear). (pdf)
*: Equal contribution; authors listed alphabeticallyXNMT: The eXtensible Neural Machine Translation Toolkit.
Graham Neubig, Matthias Sperber, Xinyi Wang, Matthieu Felix, Austin Matthews, Sarguna Padmanabhan, Ye Qi, Devendra Singh Sachan, Philip Arthur, Pierre Godard, John Hewitt, Rachid Riad, and Liming Wang.
AMTA 2018. (pdf)2017
Learning Translations via Matrix Completion.
Derry Tanti Wijaya, Brendan Callahan, John Hewitt , Xiao Ling, Marianna Apidianaki, and Chris Callison-Burch.
EMNLP 2017 (pdf)Semantic Bootstrapping in Frames: A Computational Model of Syntactic Category Acquisition.
John Hewitt, Jordan Kodner, Mitch Marcus, and Charles Yang.
Conference of the Cognitive Science Society (CogSci), (member posters) 2017. (pdf) (abstract)2016
Automatic Construction of Morphologically-Motivated Translation Models for Highly Inflected Low-Resource Languages.
John Hewitt, Matt Post, David Yarowsky.
AMTA 2016 (pdf)Research Talks
Phrasal Translation Generation for Highly Inflected, Low-Resource Languages.
Penn Computational Linguistics and Lunch (CLunch). Oct 28, 2015.A Distributional and Orthographic Aggregation Model for English Derivational Morphology.
Stanford NLP meeting. Nov 8, 2018.Patents
Capturing Rich Response Relationships with Small-Data Neural Networks.
John Hewitt.
US Patent App 15/841,963. December 2017 (submitted, pending). - Blog
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Projects
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seq2seq lecture
- I wrote a lecture on sequence-to-sequence models, focusing on visual intuition and assuming very little student background, for Penn’s computational linguistics class with Reno Kriz. Check it out and feel free to give comments.
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Massively Multilingual Image Dataset
- This large, massively multilingual dataset of images parallel to the words they represent, those words’ translation into English, and images of those English words, is described in an ACL 2018 paper.
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About
Tidbits
Scott Aaronson’s old note on frameworks for reasoning about large numbers, for enjoyment
Kevin Knight’s note on unix commands, to help you with your bash skills
I highly suggest Arch Linux for its configurability and the educational experience it provides…
Contact
Take my school email johnhew@stanford, and predict the TLD using your internal knowledge base.