"Grounded" language learning employs training data in the form of sentences paired with relevant but ambiguous perceptual contexts. Borschinger et al. (2011) introduced an approach to grounded language learning based on unsupervised PCFG induction. Their approach works well when each sentence potentially refers to one of a small set of possible meanings, such as in the sportscasting task. However, it does not scale to problems with a large set of potential meanings for each sentence, such as the navigation instruction following task studied by Chen and Mooney (2011). This paper presents an enhancement of the PCFG approach that scales to such problems with highly-ambiguous supervision. Experimental results on the navigation task demonstrates the effectiveness of our approach.

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In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL '12), pp. 433--444, Jeju Island, Korea, July 2012.

@inproceedings{kim.emnlp12, title={Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision}, author={Joohyun Kim and Raymond J. Mooney}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL '12)}, month={July}, address={Jeju Island, Korea}, pages={433--444}, url="http://www.cs.utexas.edu/users/ai-lab?kim:emnlp12", year={2012} }