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        <title>Ambiguity and Disagreement in Abstract Meaning Representation</title>
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            <title>Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation</title>
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            <namePart type="given">Michael</namePart>
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    <abstract>Abstract Meaning Representation (AMR) is a graph-based semantic formalism which has been incorporated into a number of downstream tasks related to natural language understanding. Recent work has highlighted the key, yet often ignored, role of ambiguity and implicit information in natural language understanding. As such, in order to effectively leverage AMR in downstream applications, it is imperative to understand to what extent and in what ways ambiguity affects AMR graphs and causes disagreement in AMR annotation. In this work, we examine the role of ambiguity in AMR graph structure by employing a taxonomy of ambiguity types and producing AMRs affected by each type. Additionally, we investigate how various AMR parsers handle the presence of ambiguity in sentences. Finally, we quantify the impact of ambiguity on AMR using disambiguating paraphrases at a larger scale, and compare this to the measurable impact of ambiguity in vector semantics.</abstract>
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%0 Conference Proceedings
%T Ambiguity and Disagreement in Abstract Meaning Representation
%A Wein, Shira
%Y Roth, Michael
%Y Schlechtweg, Dominik
%S Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F wein-2025-ambiguity
%X Abstract Meaning Representation (AMR) is a graph-based semantic formalism which has been incorporated into a number of downstream tasks related to natural language understanding. Recent work has highlighted the key, yet often ignored, role of ambiguity and implicit information in natural language understanding. As such, in order to effectively leverage AMR in downstream applications, it is imperative to understand to what extent and in what ways ambiguity affects AMR graphs and causes disagreement in AMR annotation. In this work, we examine the role of ambiguity in AMR graph structure by employing a taxonomy of ambiguity types and producing AMRs affected by each type. Additionally, we investigate how various AMR parsers handle the presence of ambiguity in sentences. Finally, we quantify the impact of ambiguity on AMR using disambiguating paraphrases at a larger scale, and compare this to the measurable impact of ambiguity in vector semantics.
%U https://aclanthology.org/2025.comedi-1.14/
%P 145-154

Markdown (Informal)

[Ambiguity and Disagreement in Abstract Meaning Representation](https://aclanthology.org/2025.comedi-1.14/) (Wein, CoMeDi 2025)

• Ambiguity and Disagreement in Abstract Meaning Representation (Wein, CoMeDi 2025)

ACL

• Shira Wein. 2025. Ambiguity and Disagreement in Abstract Meaning Representation. In Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation, pages 145–154, Abu Dhabi, UAE. International Committee on Computational Linguistics.