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            <namePart type="given">Danilo</namePart>
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    <abstract>This paper describes SPINDLE, an open source Python module, providing an efficient and accurate parser for written Dutch that transforms raw text input to programs for meaning composition expressed as \ensuremathłambda terms. The parser integrates a number of breakthrough advances made in recent years. Its output consists of hi-res derivations of a multimodal type-logical grammar, capturing two orthogonal axes of syntax, namely deep function-argument structures and dependency relations. These are produced by three interdependent systems: a static type-checker asserting the well-formedness of grammatical analyses, a state-of-the-art, structurally-aware supertagger based on heterogeneous graph convolutions, and a massively parallel proof search component based on Sinkhorn iterations. Packed in the software are also handy utilities and extras for proof visualization and inference, intended to facilitate end-user utilization.</abstract>
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%0 Conference Proceedings
%T SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention
%A Kogkalidis, Konstantinos
%A Moortgat, Michael
%A Moot, Richard
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F kogkalidis-etal-2023-spindle
%X This paper describes SPINDLE, an open source Python module, providing an efficient and accurate parser for written Dutch that transforms raw text input to programs for meaning composition expressed as \ensuremathłambda terms. The parser integrates a number of breakthrough advances made in recent years. Its output consists of hi-res derivations of a multimodal type-logical grammar, capturing two orthogonal axes of syntax, namely deep function-argument structures and dependency relations. These are produced by three interdependent systems: a static type-checker asserting the well-formedness of grammatical analyses, a state-of-the-art, structurally-aware supertagger based on heterogeneous graph convolutions, and a massively parallel proof search component based on Sinkhorn iterations. Packed in the software are also handy utilities and extras for proof visualization and inference, intended to facilitate end-user utilization.
%R 10.18653/v1/2023.eacl-demo.15
%U https://aclanthology.org/2023.eacl-demo.15/
%U https://doi.org/10.18653/v1/2023.eacl-demo.15
%P 128-135

Markdown (Informal)

[SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention](https://aclanthology.org/2023.eacl-demo.15/) (Kogkalidis et al., EACL 2023)

• SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention (Kogkalidis et al., EACL 2023)

ACL

• Konstantinos Kogkalidis, Michael Moortgat, and Richard Moot. 2023. SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 128–135, Dubrovnik, Croatia. Association for Computational Linguistics.