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        <title>Question-parsing with Abstract Meaning Representation enhanced by adding small datasets</title>
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        <namePart type="given">Johannes</namePart>
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    <name type="personal">
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            <title>Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)</title>
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        <name type="personal">
            <namePart type="given">Richard</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Question-parsing with Abstract Meaning Representation enhanced by adding small datasets
%A Heinecke, Johannes
%A Boritchev, Maria
%A Herledan, Frédéric
%Y Johansson, Richard
%Y Stymne, Sara
%S Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-109-0
%F heinecke-etal-2025-question
%X 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.
%U https://aclanthology.org/2025.nodalida-1.26/
%P 252-257

Markdown (Informal)

[Question-parsing with Abstract Meaning Representation enhanced by adding small datasets](https://aclanthology.org/2025.nodalida-1.26/) (Heinecke et al., NoDaLiDa 2025)

• Question-parsing with Abstract Meaning Representation enhanced by adding small datasets (Heinecke et al., NoDaLiDa 2025)

ACL

• Johannes Heinecke, Maria Boritchev, and Frédéric Herledan. 2025. Question-parsing with Abstract Meaning Representation enhanced by adding small datasets. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 252–257, Tallinn, Estonia. University of Tartu Library.