# SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention

arXiv: https://arxiv.org/abs/2302.12050 · HTML: https://mlsanity.com/paper/2302.12050/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Learning Latent Permutations with Gumbel-Sinkhorn Networks](https://mlsanity.com/paper/1802.08665/) (2018)
- [LangPro: Natural Language Theorem Prover](https://mlsanity.com/paper/1708.09417/) (2017)
- [Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories](https://mlsanity.com/paper/2012.01285/) (2020)
- [Neural Proof Nets](https://mlsanity.com/paper/2009.12702/) (2020)
- [Something Old, Something New: Grammar-based CCG Parsing with Transformer Models](https://mlsanity.com/paper/2109.10044/) (2021)
- [Constructive Type-Logical Supertagging with Self-Attention Networks](https://mlsanity.com/paper/1905.13418/) (2019)
- [AE THEL: Automatically Extracted Typelogical Derivations for Dutch](https://mlsanity.com/paper/1912.12635/) (2019)
- [Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions](https://mlsanity.com/paper/2203.12235/) (2022)
- [Static and Dynamic Vector Semantics for Lambda Calculus Models of Natural Language](https://mlsanity.com/paper/1810.11351/) (2018)

## Built on by (most cited first, 1 of 1)

- [Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization](https://mlsanity.com/paper/2305.14917/) (2023)

## Abstract (arXiv)

This paper describes SPINDLE - an open source Python module implementing 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.