mlsanityData to 7 Oct 2026

Language to Logical Form with Neural Attention

arXiv 1601.01280 ↗First posted 6 Jan 2016cs.CLcited by 158

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Abstract (arXiv)

Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations.

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 42; 2022: 32; 2023: 45; 2024: 14; 2025: 10; 2026: 14.

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38 more
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  10. 2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
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  12. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  13. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  14. 2021Neural-Symbolic Solver for Math Word Problems with Auxiliary Taskscited by 38
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  16. 2021Program Transfer for Answering Complex Questions over Knowledge Basescited by 34
  17. 2018Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedbackcited by 32
  18. 2023Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Reviewcited by 32
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  30. 2021Zero-Shot Cross-lingual Semantic Parsingcited by 19
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  37. 2021On Robustness of Neural Semantic Parserscited by 14
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