mlsanityData to 7 Oct 2026

Semantic Parsing with Semi-Supervised Sequential Autoencoders

arXiv 1609.09315 ↗First posted 29 Sep 2016cs.CL cs.AI cs.NEcited by 11

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Abstract (arXiv)

We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing. The unsupervised component is based on a generative model in which latent sentences generate the unpaired logical forms. We apply this method to a number of semantic parsing tasks focusing on domains with limited access to labelled training data and extend those datasets with synthetically generated logical forms.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2016.

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Citing papers per year: 2021: 7; 2022: 2; 2023: 2.

11 papers.

  1. 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 162
  2. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
  3. 2022Formal Specifications from Natural Languagecited by 33
  4. 2021On The Ingredients of an Effective Zero-shot Semantic Parsercited by 13
  5. 2021Localization with Sampling-Argmaxcited by 11
  6. 2021Few-Shot Semantic Parsing for New Predicatescited by 7
  7. 2021Learning from Executions for Semantic Parsingcited by 7
  8. 2021Triangular Bidword Generation for Sponsored Search Auctioncited by 5
  9. 2021Differentiable Feature Selection, a Reparameterization Approachcited by 4
  10. 2023Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsingcited by 2
  11. 2022Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Followingcited by 0