Contextual Semantic Parsing using Crowdsourced Spatial Descriptions

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...

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Bibliographic Details
Published in:arXiv.org 2014-05
Main Author: Dukes, Kais
Format: Article
Language:English
Subjects:
Crowdsourcing
Dynamic programming
Parsers
Semantics
Sentences
Upper bounds
Online Access:Get full text
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Summary: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.
ISSN:2331-8422