In order to build a simulated robot that accepts instructions in unconstrained natural language, a corpus of 427 route instructions was collected from human subjects in the office navigation domain. The instructions were segmented by the steps in the actual route and labeled with the action taken in each step. This flat formulation reduced the problem to an IE/Segmentation task, to which we applied Conditional Random Fields. We compared the performance of CRFs with a set of hand-written rules. The result showed that CRFs perform better with a 73.7% success rate.
['Nobuyuki Shimizu']
Semantic Discourse Segmentation and Labeling for Route Instructions