DRS Parsing as Sequence Labeling

Abstract

We present the first fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures (DRSs) that is competitive in accuracy with recent sequence-to-sequence models and at the same time emphcompositional in the sense that the output maps each token to one of a finite set of meaning emphfragments, and the meaning of the utterance is a function of the meanings of its parts. We argue that this property makes the system more transparent and more useful for human-in-the-loop annotation. We achieve this simply by casting DRS parsing as a sequence labeling task, where tokens are labeled with both fragments (lists of abstracted clauses with relative referent indices indicating unification) and emphsymbols like word senses or names. We give a comprehensive error analysis that highlights areas for future work.

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