Learning to sportscast: a test of grounded language acquisition
Proceedings of the 25th international conference on Machine learning
A generative model for parsing natural language to meaning representations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning semantic correspondences with less supervision
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Training a multilingual sportscaster: using perceptual context to learn language
Journal of Artificial Intelligence Research
Generative alignment and semantic parsing for learning from ambiguous supervision
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Fast online lexicon learning for grounded language acquisition
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Semantic parsing with Bayesian tree transducers
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Exploiting social information in grounded language learning via grammatical reductions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Unsupervised PCFG induction for grounded language learning with highly ambiguous supervision
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Exploring adaptor grammars for native language identification
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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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.