Paper: Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision

ACL ID D12-1040
Title Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

?Grounded? language learning employs train- ing data in the form of sentences paired with relevant but ambiguous perceptual contexts. Bo?rschinger et al. (2011) introduced an ap- proach to grounded language learning based on unsupervised PCFG induction. Their ap- proach works well when each sentence po- tentially refers to one of a small set of pos- sible meanings, such as in the sportscasting task. However, it does not scale to prob- lems with a large set of potential meanings for each sentence, such as the navigation in- struction following task studied by Chen and Mooney (2011). This paper presents an en- hancement of the PCFG approach that scales to such problems with highly-ambiguous su- pervision. Experimental results on the naviga- tion task demonstrates the effectiveness of our app...