Jordan Boyd-Graber (University of Colorado) “Thinking on your Feet: Reinforcement Learning for Incremental Language Tasks”

March 28, 2017 @ 12:00 pm – 1:15 pm
Hackerman Hall B17
3400 N Charles St
Baltimore, MD 21218
Center for Language and Speech Processing


In this talk, I’ll discuss two real-world language applications that require “thinking on your feet”: synchronous machine translation (or “machine simultaneous interpretation”) and question answering (when questions are revealed one piece at a time).  In both cases, effective algorithms for these tasks must interrupt the input stream and decide when to provide output.
Synchronous machine translation is when a sentence is being produced one word at a time in a foreign language and we want to produce a translation in English simultaneously (i.e., with as little delay between a foreign language word and its English translation). This is particularly difficult in verb-final languages like German or Japanese, where an English translation can barely begin until the verb is seen. Effective translation thus requires predictions of unseen elements of the sentence (e.g., the main verb in German and Japanese, or relative clauses in Japanese, or post-positions in Japanese). We use reinforcement learning to decide when to trust our verb predictions. It must learn to balance incorrect translation versus timely translations, and must use those predictions to translate the sentence.
For question answering, we use a specially designed dataset that challenges humans: a trivia game called quiz bowl. These questions are written so that they can be interrupted by someone who knows more about the answer; that is, harder clues are at the start of the question and easier clues are at the end of the question. We create a novel neural network system to predict answers from incomplete questions and use reinforcement learning to decide when to guess.  We are able to answer questions earlier in the questions than most college trivia contestants.
Jordan Boyd-Graber is an assistant professor in the University of Colorado Boulder’s Computer Science Department (a Colorado native), formerly serving as an assistant professor at the University of Maryland. Before joining Maryland in 2010, he did his PhD with David Blei at Princeton. Jordan’s research focus is in applying machine learning and Bayesian probabilistic models to problems that help us better understand social interaction or the human cognitive process. He and his students have won “best of” awards at NIPS (2009, 2015), NAACL (2016), and CoNLL (2015), and Jordan won the British Computing Society’s 2015 Karen Spärk Jones Award and a 2017 NSF CAREER award. His research has been funded by DARPA, IARPA, NSF, NCSES, ARL, NIH, and Lockheed Martin and has been featured by CNN, Huffington Post, New York Magazine, and the Wall Street Journal.

Center for Language and Speech Processing