Corpus-based Interpretation of Instructions in Virtual Environments

Previous approaches to instruction interpretation have required either extensive domain adaptation or manually annotated corpora. This paper presents a novel approach to instruction interpretation that leverages a large amount of unannotated, easy-to-collect data from humans interacting with a virtual world. We compare several algorithms for automatically segmenting and discretizing this data into (utterance, reaction) pairs and training a classifier to predict reactions given the next utterance. Our empirical analysis shows that the best algorithm achieves 70% accuracy on this task, with no manual annotation required.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Luciana Benotti, Martin Villalba, Tessa A. Lau, Julian A. Cerruti
• Keywords: ACL 2012, Computational Linguistics, Empirical Analysis, Utterance, Virtual World