mlsanityData to 13 Oct 2026

Controllable Semantic Parsing via Retrieval Augmentation

arXiv 2110.08458 ↗First posted 16 Oct 2021cs.CLcited by 35

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Abstract (arXiv)

In practical applications of semantic parsing, we often want to rapidly change the behavior of the parser, such as enabling it to handle queries in a new domain, or changing its predictions on certain targeted queries. While we can introduce new training examples exhibiting the target behavior, a mechanism for enacting such behavior changes without expensive model re-training would be preferable. To this end, we propose ControllAble Semantic Parser via Exemplar Retrieval (CASPER). Given an input query, the parser retrieves related exemplars from a retrieval index, augments them to the query, and then applies a generative seq2seq model to produce an output parse. The exemplars act as a control mechanism over the generic generative model: by manipulating the retrieval index or how the augmented query is constructed, we can manipulate the behavior of the parser. On the MTOP dataset, in addition to achieving state-of-the-art on the standard setup, we show that CASPER can parse queries in a new domain, adapt the prediction toward the specified patterns, or adapt to new semantic schemas without having to further re-train the model.

Key facts. Cited by 35 papers in the map (data through 13 Oct 2026). Builds on 56 of its 73 references that are in the map. First posted on arXiv 16 Oct 2021.

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56 of 73 references.

References · 56

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Citing papers per year: 2021: 2; 2022: 14; 2023: 11; 2024: 4; 2025: 3; 2026: 1.

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  2. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
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  4. 2022Diverse Demonstrations Improve In-context Compositional Generalizationcited by 137
  5. 2022PromptCap: Prompt-Guided Task-Aware Image Captioningcited by 104
  6. 2022ProGen: Progressive Zero-shot Dataset Generation via In-context Feedbackcited by 67
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  16. 2024RATSF: Empowering Customer Service Volume Management through Retrieval-Augmented Time-Series Forecastingcited by 3
  17. 2023Machine Reading Comprehension using Case-based Reasoningcited by 2
  18. 2022DocuT5: Seq2seq SQL Generation with Table Documentationcited by 1
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  20. 2025STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignmentcited by 1
  21. 2023Retrieval-Augmented Classification with Decoupled Representationcited by 0
  22. 2025Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledgecited by 0
  23. 2026Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learningcited by 0