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    Question-parsing with Abstract Meaning Representation enhanced by adding small datasets
    (University of Tartu Library, 2025-03) Heinecke, Johannes; Boritchev, Maria; Herledan, Frédéric; Johansson, Richard; Stymne, Sara
    Abstract Meaning Representation (AMR) is a graph-based formalism for representing meaning in sentences. As the annotation is quite complex, few annotated corpora exist. The most well-known and widely-used corpora are LDC’s AMR 3.0 and the datasets available on the new AMR website. Models trained on the LDC corpora work fine on texts with similar genre and style: sentences extracted from news articles, Wikipedia articles. However, other types of texts, in particular questions, are less well processed by models trained on this data. We analyse how adding few sentence-type specific annotations can steer the model to improve parsing in the case of questions in English.