• Will Monroe

• Research scientist, Duolingo
• B.S. 2013, M.S. 2015, Ph.D. 2018
• (Stanford University)

Hi everybody!

I'm Will. My favorite pastime is learning all the languages of the world and trying to teach them to others. I also sometimes do research and write code. Since September 2018 I've been using natural language processing to improve the coverage and quality of Duolingo's 100+ language courses.

Before coming to Duolingo, I did my Ph.D. and undergraduate studies at Stanford University. My Ph.D. research focused mainly on computational pragmatics and natural language grounding with variants of the Rational Speech Act model, advised by Christopher Potts. I've also published papers with Noah Goodman, Dan Jurafsky, and Christopher Manning. My undergraduate honors thesis, advised by Ryan Williams and Virginia Vassilevska Williams, was on matrix multiplication algorithms.

I spent two summers working with language revitalization programs for the Alutiiq and Unangam Tunuu languages in Kodiak and Anchorage, Alaska, using the Where are Your Keys? immersive language learning game.

Some things I got into in college that I still enjoy today include music, dance, Kenpo Karate, and simply learning languages for fun (easiest way to put a smile on my face: start talking to me in Arabic, Mandarin, or Alutiiq).

This page includes my research, teaching materials, some past coursework, and a few other side projects. It occasionally gets out of date, so please email me before using details from this site in other places.

• Publications

  [Google Scholar] [Semantic Scholar]

  2024
  • Austin Xu, Will Monroe, and Klinton Bicknell. 2024. Large Language Model Augmented Exercise Retrieval for Personalized Language Learning. In Proceedings of the 14th International Learning Analytics and Knowledge Conference (LAK). [arXiv] [ACM] [PDF] [BibTeX]
  • (Workshop version) Austin Xu, Klinton Bicknell, and Will Monroe. 2023. Large Language Model Augmented Exercise Retrieval for Personalized Language Learning. In Proceedings of the NeurIPS Workshop on Generative AI for Education (GAIED). [PDF] [BibTeX] [poster]
  2020
  • Stephen Mayhew, Klinton Bicknell, Chris Brust, Bill McDowell, Will Monroe, and Burr Settles. 2020. Simultaneous Translation And Paraphrase for Language Education. In Proceedings of the ACL Workshop on Neural Generation and Translation (WNGT). [PDF] [BibTeX]
  2018
  • Will Monroe. 2018. Learning in the Rational Speech Acts Model. Ph.D. dissertation, Stanford University. [PDF] [BibTeX]
  • Arianna Yuan, Will Monroe, Yu Bai, and Nate Kushman. 2018. Understanding the Rational Speech Act Model. In Proceedings of the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018). [CogSci] [PDF] [BibTeX]
  • Will Monroe, Jennifer Hu, Andrew Jong, and Christopher Potts. 2018. Generating Bilingual Pragmatic Color References. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018). [arXiv] [PDF] [BibTeX] [poster] [data] [code]
  2017
  • Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, and Dan Jurafsky. 2017. Adversarial Learning for Neural Dialogue Generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017). [arXiv] [PDF] [BibTeX] [code]
  • Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, and Christopher Potts. 2017. Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding. Transactions of the Association for Computational Linguistics (TACL) 5, pp. 325–338. [TACL] [arXiv] [PDF] [BibTeX] [data] [code]
  2016
  • Amir Goldberg, Sameer B. Srivastava, V. Govind Manian, William Monroe, and Christopher Potts. 2016. Fitting In or Standing Out? The Tradeoffs of Structural and Cultural Embeddedness. American Sociological Review 81(6), pp. 1190–1222. [SAGE] [PDF manuscript] [BibTeX]
  • Will Monroe, Noah D. Goodman, and Christopher Potts. 2016. Learning to Generate Compositional Color Descriptions. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016). [arXiv] [PDF] [BibTeX] [code]
  • Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep Reinforcement Learning for Dialogue Generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016). [arXiv]
  2015
  • Will Monroe and Christopher Potts. 2015. Learning in the Rational Speech Acts Model. In Proceedings of the 20th Amsterdam Colloquium. [arXiv] [PDF] [BibTeX] [code]
  • Angel Chang, Will Monroe, Manolis Savva, Christopher Potts, and Christopher D. Manning. 2015. Text to 3D Scene Generation with Rich Lexical Grounding. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2015). [arXiv] [PDF] [BibTeX] [data]
  2014
  • Will Monroe, Spence Green, and Christopher D. Manning. 2014. Word Segmentation of Informal Arabic with Domain Adaptation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014): Short Papers. [PDF] [BibTeX] [code]

  Other Research

  • Liszt— A language for parallel scientific computing
  • Honors thesis— Automating the Search for Faster Matrix Multiplication Algorithms
  • CURIS 2011— Meru: Building a Virtual City

• Teaching

  • Splash!
  • CS 106— Programming Methodology and Abstractions
  • CS 109— Introduction to Probability for Computer Scientists

  Coursework

  • CS 224U— Natural Language Understanding
  • CS 248— Interactive Computer Graphics

  Other

  • Morse Code
  • Eclipse 2017