  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 combination with contextual word
embeddings to enable the training of a semantic parser with little data and
without domain specific parser engineering. Key to our approach is the use of
an anonymized target representation which is more easily learned by the parser.
In most cases, this simplified representation can trivially be transformed into
an executable format, and in others the parse can be completed through further
interaction with the user. We evaluate this approach in the context of the
RoboCup@Home General Purpose Service Robot task, where we have collected a
corpus of paraphrased versions of commands from the standardized command
generator. Our results show that neural semantic parsers can predict the
logical form of unseen commands with 89% accuracy. We release our data and the
details of our models to encourage further development from the RoboCup and
service robotics communities.
