# Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2010.06115 · HTML: https://mlsanity.com/paper/2010.06115/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [RevCore: Review-augmented Conversational Recommendation](https://mlsanity.com/paper/2106.00957/) (2021)
- [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021)
- [Something Old, Something New: Grammar-based CCG Parsing with Transformer Models](https://mlsanity.com/paper/2109.10044/) (2021)
- [Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions](https://mlsanity.com/paper/2203.12235/) (2022)
- [Recurrent Visual Feature Extraction and Stereo Attentions for CT Report Generation](https://mlsanity.com/paper/2506.19665/) (2025)
- [Computed Tomography Visual Question Answering with Cross-modal Feature Graphing](https://mlsanity.com/paper/2507.04333/) (2025)
- [Scaling Hindi Quantum Natural Language Processing through Automatic Pregroup Supertagging](https://mlsanity.com/paper/2609.13721/) (2026)

## Abstract (arXiv)

Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e.g., bi-LSTM). In this paper, we propose attentive graph convolutional networks to enhance neural CCG supertagging through a novel solution of leveraging contextual information. Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly. The experiments performed on the CCGbank demonstrate that our approach outperforms all previous studies in terms of both supertagging and parsing. Further analyses illustrate the effectiveness of each component in our approach to discriminatively learn from word pairs to enhance CCG supertagging.