Paper: Adapting Discriminative Reranking to Grounded Language Learning

ACL ID P13-1022
Title Adapting Discriminative Reranking to Grounded Language Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

We adapt discriminative reranking to im- prove the performance of grounded lan- guage 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 set- ting. Instead, we show how the weak su- pervision of response feedback (e.g. suc- cessful task completion) can be used as an alternative, experimentally demonstrat- ing that its performance is comparable to training on gold-standard parse trees.