Learning to Transpile AMR into SPARQL

We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA). This allows to delegate part of the abstraction problem to a strongly pre-trained semantic parser, while learning transpiling with small amount of paired data. We departure from recent work relating AMR and SPARQL constructs, but rather than applying a set of rules, we teach the 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 progress in AMR-based KBQA in LC-QuAD (F1 53.4), matching it in QALD (F1 30.8), while exploiting the same inductive biases.