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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 λ 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 λ 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.