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Simpler Context-Dependent Logical Forms via Model Projections

arXiv 1606.05378 ↗First posted 16 Jun 2016cs.CLcited by 33

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Abstract (arXiv)

We consider the task of learning a context-dependent mapping from utterances to denotations. With only denotations at training time, we must search over a combinatorially large space of logical forms, which is even larger with context-dependent utterances. To cope with this challenge, we perform successive projections of the full model onto simpler models that operate over equivalence classes of logical forms. Though less expressive, we find that these simpler models are much faster and can be surprisingly effective. Moreover, they can be used to bootstrap the full model. Finally, we collected three new context-dependent semantic parsing datasets, and develop a new left-to-right parser.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jun 2016.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 10; 2024: 3; 2025: 4.

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  9. 2022LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-trainingcited by 5
  10. 2023The Role of Semantic Parsing in Understanding Procedural Textcited by 4
  11. 2023History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modelingcited by 4
  12. 2023Can You Follow Me? Testing Situational Understanding in ChatGPTcited by 4
  13. 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspectivecited by 3
  14. 2024SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learningcited by 3
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  18. 2025Large Language Models for IT Automation Tasks: Are We There Yet?cited by 1
  19. 2022Can We Generate Shellcodes via Natural Language? An Empirical Studycited by 0
  20. 2023Efficient Data Fusion using the Tsetlin Machinecited by 0
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