Publication
ACL 2012
Conference paper

Corpus-based interpretation of instructions in virtual environments

Abstract

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. © 2012 Association for Computational Linguistics.

Date

01 Dec 2012

Publication

ACL 2012

Authors

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