NDLI: Understanding Instructions on Large Scale for Human-Robot Interaction

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IAT 2014 Non-Program Committee Reviewers

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Understanding Instructions on Large Scale for Human-Robot Interaction

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IAT 2014 Author Index

IAT 2014 Publisher's Information

Understanding Instructions on Large Scale for Human-Robot Interaction

Content Provider	ACM Digital Library
Author	Chen, Xiaoping Xie, Jiongkun
Abstract	Correctly interpreting human instructions is the first step to human-robot interaction. Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain. Annotating large enough instructions with semantic forms needs exhaustive engineering efforts. Hence, we propose propagating the semantic lexicon to learn a semantic parser from limited annotations, whereas the parser still has the ability of interpreting instructions on a large scale. We assume that the semantically-close words have the same semantic form based on the fact that human usually uses different words to refer to a same object or task. Our approach softly maps the unobserved words/phrases to the semantic forms learned from the annotated copurs through a metric for knowledge-based lexical similarity. Experiments on the collected instructions showed that the semantic parser learned with lexicon propagation outperformed the baseline. Our approach provides an opportunity for the robots to understand the human instructions on a large scale.
Starting Page	175
Ending Page	182
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.165
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Human-robot interaction instruction understanding semantic parsing lexicon propagation graph-based semi-supervised learning
Content Type	Text
Resource Type	Article

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