Adapting Discriminative Reranking to Grounded Language Learning (2013)
We adapt discriminative reranking to improve the performance of grounded language acquisition, specifically the task of learning to follow navigation instructions from observation. Unlike conventional reranking used in syntactic and semantic parsing, gold-standard reference trees are not naturally available in a grounded setting. Instead, we show how the weak supervision of response feedback (e.g. successful task completion) can be used as an alternative, experimentally demonstrating that its performance is comparable to training on gold-standard parse trees.
In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), pp. 218--227, Sofia, Bulgaria, August 2013.

Slides (PPT)
Joohyun Kim Ph.D. Alumni scimitar [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu