# Learning Character-level Compositionality with Visual Features

arXiv: https://arxiv.org/abs/1704.04859 · HTML: https://mlsanity.com/paper/1704.04859/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2017.

## Built on by (most cited first, 12 of 12)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [Robust Open-Vocabulary Translation from Visual Text Representations](https://mlsanity.com/paper/2104.08211/) (2021)
- [PIXAR: Auto-Regressive Language Modeling in Pixel Space](https://mlsanity.com/paper/2401.03321/) (2024)
- [Sub-Character Tokenization for Chinese Pretrained Language Models](https://mlsanity.com/paper/2106.00400/) (2021)
- [Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations](https://mlsanity.com/paper/2507.04886/) (2025)
- [Parallel Corpora for Machine Translation in Low-resource Indic Languages: A Comprehensive Review](https://mlsanity.com/paper/2503.04797/) (2025)
- [Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training](https://mlsanity.com/paper/2305.18760/) (2023)
- [MIXAR: Scaling Autoregressive Pixel-based Language Models to Multiple Languages and Scripts](https://mlsanity.com/paper/2604.11575/) (2026)
- [StyleBERT: Chinese pretraining by font style information](https://mlsanity.com/paper/2202.09955/) (2022)
- [Logographic Information Aids Learning Better Representations for Natural Language Inference](https://mlsanity.com/paper/2211.02136/) (2022)
- [FFN: a Fine-grained Chinese-English Financial Domain Parallel Corpus](https://mlsanity.com/paper/2406.18856/) (2024)

## Abstract (arXiv)

Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.