Symbolic Priors for RNN-based Semantic Parsing
| Autor: | Marc Dymetman, Chunyang Xiao, Claire Gardent |
|---|---|
| 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: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Parsing Computer Science - Computation and Language Grammar Intersection (set theory) Computer science business.industry media_common.quotation_subject 020207 software engineering 02 engineering and technology computer.software_genre Automaton Recurrent neural network 0202 electrical engineering electronic engineering information engineering Logical form 020201 artificial intelligence & image processing S-attributed grammar [INFO]Computer Science [cs] Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing Natural language media_common |
| Zdroj: | wenty-sixth International Joint Conference on Artificial Intelligence (IJCAI-17) wenty-sixth International Joint Conference on Artificial Intelligence (IJCAI-17), Aug 2017, Melbourne, Australia. pp.4186-4192, ⟨10.24963/ijcai.2017/585⟩ IJCAI |
| Popis: | Seq2seq models based on Recurrent Neural Networks (RNNs) have recently received a lot of attention in the domain of Semantic Parsing. While in principle they can be trained directly on pairs (natural language utterances, logical forms), their performance is limited by the amount of available data. To alleviate this problem, we propose to exploit various sources of prior knowledge: the well-formedness of the logical forms is modeled by a weighted context-free grammar; the likelihood that certain entities present in the input utterance are also present in the logical form is modeled by weighted finite-state automata. The grammar and automata are combined together through an efficient intersection algorithm to form a soft guide (“background”) to the RNN.We test our method on an extension of the Overnight dataset and show that it not only strongly improves over an RNN baseline, but also outperforms non-RNN models based on rich sets of hand-crafted features. |
| Databáze: | OpenAIRE |
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