mohit iyyer

miyyer@cs.umass.edu // CV // Scholar // github // twitter



I am an assistant professor in computer science at UMass Amherst. Previously, I was a Young Investigator at AI2; before that, I completed my PhD at the University of Maryland, College Park, advised by Jordan Boyd-Graber and Hal Daumé III. My research interests lie broadly in natural language processing and machine learning. Much of my work uses deep learning to model language at the discourse level. Problems in this vein that I'm currently excited about include:

(1) generating long, coherent units of text
(2) answering questions about documents
(3) understanding narratives in fictional text

  • Nov. 2018: talk at WPI CS Colloquium
  • Oct. 2018: talk at UVM Symposium on the Science of Stories
  • Aug. 2018: three papers to appear at EMNLP 2018 (QuAC, (un)interpretability, and sentiment reproducibility)
  • Jul. 2018: talk at TTIC Language Generation workshop
  • Jun. 2018: ELMo won best long paper at NAACL 2018!
  • Mar. 2018: talk at USC/ISI NL Seminar
  • Feb. 2018: three papers to appear at NAACL 2018 (adversarial paraphrasing, ELMo, and image colorization)
  • Feb. 2018: talk at Ursinus College on applications of machine learning to the digital humanities
  • Jan. 2018: talk at Indian Institute of Science, Bengaluru
  • Oct. 2017: submit your QA system to our human-computer QA competition at NIPS 2017!
  • Apr. 2017: COMICS data and code released here!
  • Jan. 2017: talk at CU Boulder Stats, Optimization, and Machine Learning seminar
  • Nov. 2016: talk at UMass Machine Learning & Friends Lunch
  • Nov. 2016: new paper on understanding comic book narratives and characters.
  • Nov. 2016: new paper and associated dataset for sequential semantic parsing.
  • Jun. 2016: our paper on characterizing fictional relationships won best long paper at NAACL 2016!
  • Apr. 2016: we are organizing a workshop at NAACL 2016 on human-computer question answering with great invited speakers and accepted papers!
  • May 2015: our quiz bowl robot recently faced off against a team of four Jeopardy champions. Watch the introduction to learn how it works and then check out the actual match! If you're interested, code for the entire system is also available.

TEACHING      Fall 2018: Introduction to Natural Language Processing (CS 585)

publications