Generative Models of Grounded Language Learning with Ambiguous Supervision
Joohyun Kim · 2012
“Grounded ” language learning is the process of learning the semantics of natural language with respect to relevant perceptual inputs. Toward this goal, computational systems are trained with data in the form of natural language sentences paired with relevant but ambiguous perceptual contexts. With such ambiguous supervision, it is required to resolve the ambiguity between a natural language (NL) sentence and a corresponding set of possible logical meaning representations (MR). My research focuses on devising effective models for simultaneously disambiguating such supervision and learning the underlying semantics of language to map NL sentences into proper logical forms. Specifically, I will present two probabilistic generative models for learning such correspondences. The models are applied to two publicly available datasets in two different domains, sportscasting and navigation, and compared with previous work on the same data. I will first present a probabilistic generative model that learns the mappings from NL sentences into logical forms where the true meaning of each NL sentence is one of a handful of candidate logical MRs. It simultaneously disambiguates the meaning of each sentence in the training data and learns to probabilistically map a NL sentence to its MR form depicted in a single tree structure. Evaluations are