Name: Thinking on your Feet: Reinforcement Learning for Incremental Language Tasks
Start: 2014-09-23T18:30:00-06:00
End: 2014-09-23T21:30:00-06:00
Location: CU - Hellems 141

Professor Jordan Boyd-Graber of the University of Colorado Computer Science Department will be presenting his research on reinforcement learning for Natural Language Processing.

Venue:

• Hellems Building, HLMS 141 (http://www.colorado.edu/campusmap/map.html?bldg=HLMS), Broadway and Euclid (adjacent to the UMC)

• Room 141 is in the Northwest corner of Hellems on the ground floor

• The closest paid parking is at the Universal Memorial Center (UMC)

Schedule:

• 6:30-6:45: Meet and greet, (re)introductions

• 6:45-7:45: Presentation and discussion

• 7:45: Head out for dinner and socializing

Title: Thinking on your Feet: Reinforcement Learning for Incremental Language Tasks

Abstract:

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 recursive neural network 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.

This talk covers three papers presented at the Empirical Methods in Natural Language Processing conference.

Speaker Bio:

Jordan Boyd-Graber is an assistant professor in the University of Colorado Boulder's Computer Science Department, formerly serving as an assistant professor at the University of Maryland. He is a 2010 graduate of Princeton University, with a PhD thesis on "Linguistic Extensions of Topic Models" working under David Blei. 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. This research often leads him to use tools such as large-scale inference for probabilistic methods, natural language processing, multilingual corpus understanding, and human computation.

Lee Becker

Front Range NLP (Natural Language Processing)

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Thinking on your Feet: Reinforcement Learning for Incremental Language Tasks

CU - Hellems 141

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Front Range NLP (Natural Language Processing)

Thinking on your Feet: Reinforcement Learning for Incremental Language Tasks

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