Contextual Semantic Parsing using Crowdsourced Spatial Descriptions
This addresses disambiguation in semantic parsing for robot commands, but it is incremental as it builds on existing parsing methods with added context.
The paper tackles the problem of semantic parsing with situational context to disambiguate sentence readings, achieving a 96.53% exact-match score for sentences recognized by the planner, compared to 82.14% for a non-contextual parser.
We describe a contextual parser for the Robot Commands Treebank, a new crowdsourced resource. In contrast to previous semantic parsers that select the most-probable parse, we consider the different problem of parsing using additional situational context to disambiguate between different readings of a sentence. We show that multiple semantic analyses can be searched using dynamic programming via interaction with a spatial planner, to guide the parsing process. We are able to parse sentences in near linear-time by ruling out analyses early on that are incompatible with spatial context. We report a 34% upper bound on accuracy, as our planner correctly processes spatial context for 3,394 out of 10,000 sentences. However, our parser achieves a 96.53% exact-match score for parsing within the subset of sentences recognized by the planner, compared to 82.14% for a non-contextual parser.