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Neural Semantic Parsing with Anonymization for Command Understanding in General-Purpose Service Robots
[article]
2019
pre-print
Service robots are envisioned to undertake a wide range of tasks at the request of users. Semantic parsing is one way to convert natural language commands given to these robots into executable representations. Methods for creating semantic parsers, however, rely either on large amounts of data or on engineered lexical features and parsing rules, which has limited their application in robotics. To address this challenge, we propose an approach that leverages neural semantic parsing methods in
doi:10.1007/978-3-030-35699-6_26
arXiv:1907.01115v1
fatcat:ajxiik47znaybhgfsrdlkqv24e