Sequence-based Structured Prediction for Semantic Parsing
| Autor: | Claire Gardent, Chunyang Xiao, Marc Dymetman |
|---|---|
| Přispěvatelé: | Xerox Research Centre Europe [Meylan], Xerox Company, Natural Language Processing : representations, inference and semantics (SYNALP), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Sequence
Parsing Artificial neural network Computer science Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Prefix TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES ACM: H.: Information Systems Symbol (programming) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Structured prediction Representation (mathematics) Algorithm computer Encoder 0105 earth and related environmental sciences |
| Zdroj: | Annual meeting of the Association for Computational Linguistics (ACL) Annual meeting of the Association for Computational Linguistics (ACL), Aug 2016, Berlin, Germany. pp.1341-1350, ⟨10.18653/v1/P16-1127⟩ ACL (1) |
| Popis: | International audience; We propose an approach for semantic parsing that uses a recurrent neural network to map a natural language question into a logical form representation of a KB query. Building on recent work by (Wang et al., 2015), the interpretable logical forms, which are structured objects obeying certain constraints, are enumerated by an underlying grammar and are paired with their canonical realizations. In order to use sequence prediction, we need to sequentialize these logical forms. We compare three sequentializations: a direct linearization of the logical form, a linearization of the associated canonical realization, and a sequence consisting of derivation steps relative to the underlying grammar. We also show how grammatical constraints on the derivation sequence can easily be integrated inside the RNN-based sequential predictor. Our experiments show important improvements over previous results for the same dataset, and also demonstrate the advantage of incorporating the grammatical constraints. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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