# Generating CCG Categories

arXiv: https://arxiv.org/abs/2103.08139 · HTML: https://mlsanity.com/paper/2103.08139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [Semi-Supervised Sequence Modeling with Cross-View Training](https://mlsanity.com/paper/1809.08370/) (2018)
- [Grammar as a Foreign Language](https://mlsanity.com/paper/1412.7449/) (2014)
- [Straight to the Tree: Constituency Parsing with Neural Syntactic Distance](https://mlsanity.com/paper/1806.04168/) (2018)
- [In-Order Transition-based Constituent Parsing](https://mlsanity.com/paper/1707.05000/) (2017)
- [A* CCG Parsing with a Supertag and Dependency Factored Model](https://mlsanity.com/paper/1704.06936/) (2017)
- [Left-to-Right Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/1903.08445/) (2019)
- [Dependency Parsing as Head Selection](https://mlsanity.com/paper/1606.01280/) (2016)
- [Structured Training for Neural Network Transition-Based Parsing](https://mlsanity.com/paper/1506.06158/) (2015)
- [Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories](https://mlsanity.com/paper/2012.01285/) (2020)
- [Constructive Type-Logical Supertagging with Self-Attention Networks](https://mlsanity.com/paper/1905.13418/) (2019)
- [A Dynamic Window Neural Network for CCG Supertagging](https://mlsanity.com/paper/1610.02749/) (2016)
- [Shortcut Sequence Tagging](https://mlsanity.com/paper/1701.00576/) (2017)

## Built on by (most cited first, 2 of 2)

- [Revisiting Supertagging for Faster HPSG Pasing](https://mlsanity.com/paper/2309.07590/) (2023)
- [Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions](https://mlsanity.com/paper/2203.12235/) (2022)

## Abstract (arXiv)

Previous CCG supertaggers usually predict categories using multi-class classification. Despite their simplicity, internal structures of categories are usually ignored. The rich semantics inside these structures may help us to better handle relations among categories and bring more robustness into existing supertaggers. In this work, we propose to generate categories rather than classify them: each category is decomposed into a sequence of smaller atomic tags, and the tagger aims to generate the correct sequence. We show that with this finer view on categories, annotations of different categories could be shared and interactions with sentence contexts could be enhanced. The proposed category generator is able to achieve state-of-the-art tagging (95.5% accuracy) and parsing (89.8% labeled F1) performances on the standard CCGBank. Furthermore, its performances on infrequent (even unseen) categories, out-of-domain texts and low resource language give promising results on introducing generation models to the general CCG analyses.