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.

我们介绍了 MASSIVE-AMR 数据集，它是迄今最大且最多样化的 AMR 数据集，包含超过 84,000 个文本到图形的注释，映射到 50 多种具有不同语言类型的信息寻求话语，并使用大型语言模型进行多语言 AMR 和 SPARQL 分析的实验，以及在知识库问答上应用 AMR 进行虚构检测的结果，对使用 LLMs 进行结构化分析的持续问题提供了一些启示。