Broad-Coverage Semantic Parsing as Transduction

We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectiv...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Zhang, Sheng, Ma, Xutai, Duh, Kevin, Benjamin Van Durme
Format: Article
Language:English
Subjects:
Semantics
Online Access:Get full text
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Summary:We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectively trained without relying on a pre-trained aligner. Experiments conducted on three separate broad-coverage semantic parsing tasks -- AMR, SDP and UCCA -- demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.
ISSN:2331-8422