Adapting Discriminative Reranking to Grounded Language Learning
Joohyun Kim, Raymond J. Mooney · 2013
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. 1