# Efficient Multilingual Name Type Classification Using Convolutional Networks

arXiv: https://arxiv.org/abs/2601.11090 · HTML: https://mlsanity.com/paper/2601.11090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 16 Jan 2026.

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

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://mlsanity.com/paper/2201.06642/) (2022)
- [Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark](https://mlsanity.com/paper/2311.09122/) (2023)
- [Design Challenges in Named Entity Transliteration](https://mlsanity.com/paper/1808.02563/) (2018)
- [JRC-Names: A freely available, highly multilingual named entity resource](https://mlsanity.com/paper/1309.6162/) (2013)
- [ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages using Wikidata](https://mlsanity.com/paper/2405.09496/) (2024)
- [AyutthayaAlpha: A Thai-Latin Script Transliteration Transformer](https://mlsanity.com/paper/2412.03877/) (2024)

## Abstract (arXiv)

We present a convolutional neural network approach for classifying proper names by language and entity type. Our model, Onomas-CNN X, combines parallel convolution branches with depthwise-separable operations and hierarchical classification to process names efficiently on CPU hardware. We evaluate the architecture on a large multilingual dataset covering 104 languages and four entity types (person, organization, location, other). Onomas-CNN X achieves 92.1% accuracy while processing 2,813 names per second on a single CPU core - 46 times faster than fine-tuned XLM-RoBERTa with comparable accuracy. The model reduces energy consumption by a factor of 46 compared to transformer baselines. Our experiments demonstrate that specialized CNN architectures remain competitive with large pre-trained models for focused NLP tasks when sufficient training data exists.