It is often assumed that 'grounded' learning tasks are beyond the scope of grammatical inference techniques. In this paper, we show that the grounded task of learning a semantic parser from ambiguous training data as discussed in Kim and Mooney (2010) can be reduced to a Probabilistic Context-Free Grammar learning task in a way that gives state of the art results. We further show that additionally letting our model learn the language's canonical word order improves its performance and leads to the highest semantic parsing f-scores previously reported in the literature.
REF casted the problem of learning a semantic parser as a PCFG induction task, achieving state-of the art performance in the RoboCup domain.
Reducing Grounded Learning Tasks To Grammatical Inference
EMNLP
Computer Science