Reducing Grounded Learning Tasks To Grammatical Inference

Abstract

It is often assumed that ‘grounded ’ learning tasks are beyond the scope of grammatical in-ference techniques. In this paper, we show that the grounded task of learning a seman-tic parser from ambiguous training data as dis-cussed in Kim and Mooney (2010) can be re-duced to a Probabilistic Context-Free Gram-mar learning task in a way that gives state of the art results. We further show that ad-ditionally letting our model learn the lan-guage’s canonical word order improves its performance and leads to the highest seman-tic parsing f-scores previously reported in the literature.1 1