mlsanityData to 7 Oct 2026

Few-Shot Semantic Parsing for New Predicates

arXiv 2101.10708 ↗First posted 26 Jan 2021cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

In this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with utterance-logical form pairs per new predicate. The state-of-the-art neural semantic parsers achieve less than 25% accuracy on benchmark datasets when k= 1. To tackle this problem, we proposed to i) apply a designated meta-learning method to train the model; ii) regularize attention scores with alignment statistics; iii) apply a smoothing technique in pre-training. As a result, our method consistently outperforms all the baselines in both one and two-shot settings.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 39 of its 62 references that are in the map. First posted on arXiv 26 Jan 2021.

Builds on

39 of 62 references.

References · 39

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27 more
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Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 1.

7 papers.

  1. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 105
  2. 2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
  3. 2021Total Recall: a Customized Continual Learning Method for Neural Semantic Parserscited by 9
  4. 2022WildQA: In-the-Wild Video Question Answeringcited by 9
  5. 2022Paraphrasing Techniques for Maritime QA systemcited by 7
  6. 2023Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approachcited by 7
  7. 2022When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systemscited by 2