# Constructive Type-Logical Supertagging with Self-Attention Networks

arXiv: https://arxiv.org/abs/1905.13418 · HTML: https://mlsanity.com/paper/1905.13418/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 5 of 5)

- [Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions](https://mlsanity.com/paper/2203.12235/) (2022)
- [Generating CCG Categories](https://mlsanity.com/paper/2103.08139/) (2021)
- [Comparing in context: Improving cosine similarity measures with a metric tensor](https://mlsanity.com/paper/2203.14996/) (2022)
- [SPINDLE: Spinning Raw Text into Lambda Terms with Graph Attention](https://mlsanity.com/paper/2302.12050/) (2023)
- [A Logic-Based Framework for Natural Language Inference in Dutch](https://mlsanity.com/paper/2110.03323/) (2021)

## Abstract (arXiv)

We propose a novel application of self-attention networks towards grammar induction. We present an attention-based supertagger for a refined type-logical grammar, trained on constructing types inductively. In addition to achieving a high overall type accuracy, our model is able to learn the syntax of the grammar's type system along with its denotational semantics. This lifts the closed world assumption commonly made by lexicalized grammar supertaggers, greatly enhancing its generalization potential. This is evidenced both by its adequate accuracy over sparse word types and its ability to correctly construct complex types never seen during training, which, to the best of our knowledge, was as of yet unaccomplished.