@inproceedings{pavlova-etal-2024-yarn,
    title = "{YARN} is All You Knit: Encoding Multiple Semantic Phenomena with Layers",
    author = "Pavlova, Siyana  and
      Amblard, Maxime  and
      Guillaume, Bruno",
    editor = "Bonial, Claire  and
      Bonn, Julia  and
      Hwang, Jena D.",
    booktitle = "Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.dmr-1.8",
    pages = "66--76",
    abstract = "In this paper, we present the first version of YARN, a new semantic representation formalism. We propose this new formalism to unify the advantages of logic-based formalisms while retaining direct interpretation, making it widely usable. YARN is rooted in the encoding of different semantic phenomena as separate layers. We begin by presenting a formal definition of the mathematical structure that constitutes YARN. We then illustrate with concrete examples how this structure can be used in the context of semantic representation for encoding multiple phenomena (such as modality, negation and quantification) as layers built on top of a central predicate-argument structure. The benefit of YARN is that it allows for the independent annotation and analysis of different phenomena as they are easy to {``}switch off{''}. Furthermore, we have explored YARN{'}s ability to encode simple interactions between phenomena. We wrap up the work presented by a discussion of some of the interesting observations made during the development of YARN so far and outline our extensive future plans for this formalism.",
}

In this paper, we present the first version of YARN, a new semantic representation formalism. We propose this new formalism to unify the advantages of logic-based formalisms while retaining direct interpretation, making it widely usable. YARN is rooted in the encoding of different semantic phenomena as separate layers. We begin by presenting a formal definition of the mathematical structure that constitutes YARN. We then illustrate with concrete examples how this structure can be used in the context of semantic representation for encoding multiple phenomena (such as modality, negation and quantification) as layers built on top of a central predicate-argument structure. The benefit of YARN is that it allows for the independent annotation and analysis of different phenomena as they are easy to {``}switch off{''}. Furthermore, we have explored YARN{'}s ability to encode simple interactions between phenomena. We wrap up the work presented by a discussion of some of the interesting observations made during the development of YARN so far and outline our extensive future plans for this formalism.
Pavlova, Siyana Amblard, Maxime Guillaume, Bruno
YARN is All You Knit: Encoding Multiple Semantic Phenomena with Layers