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Learning to Transpile AMR into SPARQL
[article]
2022
arXiv
pre-print
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
arXiv:2112.07877v2
fatcat:ebriqayim5ftrls4gaqxv7mx4u