mlsanityData to 7 Oct 2026

Learning to Transpile AMR into SPARQL

arXiv 2112.07877 ↗First posted 15 Dec 2021cs.CLcited by 4

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Abstract (arXiv)

We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). This allows us to delegate part of the semantic representation to a strongly pre-trained semantic parser, while learning transpiling with small amount of paired data. We depart from recent work relating AMR and SPARQL constructs, but rather than applying a set of rules, we teach a BART model to selectively use these relations. Further, we avoid explicitly encoding AMR but rather encode the parser state in the attention mechanism of BART, following recent semantic parsing works. The resulting model is simple, provides supporting text for its decisions, and outperforms recent approaches in KBQA across two knowledge bases: DBPedia (LC-QuAD 1.0, QALD-9) and Wikidata (WebQSP, SWQ-WD).

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

24 of 45 references.

References · 24

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12 more
  1. 2018Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answeringcited by 30
  2. 2021Maximum Bayes Smatch Ensemble Distillation for AMR Parsingcited by 27
  3. 2021AMR Parsing with Action-Pointer Transformercited by 25
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  12. 2021SPARQLing Database Queries from Intermediate Question Decompositionscited by 7

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 1.

4 papers.

  1. 2022FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representationscited by 41
  2. 2022Knowledge Base Question Answering: A Semantic Parsing Perspectivecited by 22
  3. 2022GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representationcited by 13
  4. 2024MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detectioncited by 4