A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing

Andreas Vlachos, Stephen Clark

Abstract


Semantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation. Most approaches to this task have been evaluated on a small number of existing corpora which assume that all utterances must be interpreted according to a database and typically ignore context. In this paper we present a new, publicly available corpus for context-dependent semantic parsing. The MRL used for the annotation was designed to support a portable, interactive tourist information system. We develop a semantic parser for this corpus by adapting the imitation learning algorithm DAGGER without requiring alignment information during training. DAGGER improves upon independently trained classifiers by 9.0 and 4.8 points in F-score on the development and test sets respectively.


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