  Abstract Meaning Representation (AMR) is a semantic formalism that captures
the core meaning of an utterance. There has been substantial work developing
AMR corpora in English and more recently across languages, though the limited
size of existing datasets and the cost of collecting more annotations are
prohibitive. With both engineering and scientific questions in mind, we
introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph
annotations, currently the largest and most diverse of its kind: AMR graphs for
1,685 information-seeking utterances mapped to 50+ typologically diverse
languages. We describe how we built our resource and its unique features before
reporting on experiments using large language models for multilingual AMR and
SPARQL parsing as well as applying AMRs for hallucination detection in the
context of knowledge base question answering, with results shedding light on
persistent issues using LLMs for structured parsing.
