Situated Language Understanding at 25 Miles per Hour
| Teruhisa Misu, Antoine Raux, Rakesh Gupta and Ian Lane |
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In this paper, we address issues in situated language understanding in a rapidly changing environment – a moving car. Specifically, we propose methods for understanding user queries about specific target buildings in their surroundings. Unlike previous studies on physically situated interactions such as interaction with mobile robots, the task is very sensitive to timing because the spatial relation between the car and the target is changing while the user is speaking. We collected situated utterances from drivers using our research system, Townsurfer, which is embedded in a real vehicle. Based on this data, we analyze the timing of user queries, spatial relationships between the car and targets, head pose of the user, and linguistic cues. Optimized on the data, our algorithms improved the target identification rate by 24.1% absolute.
