# Sources of Transfer in Multilingual Named Entity Recognition

arXiv: https://arxiv.org/abs/2005.00847 · HTML: https://mlsanity.com/paper/2005.00847/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 9 of 9)

- [CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation](https://mlsanity.com/paper/2210.07022/) (2022)
- [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://mlsanity.com/paper/2406.04215/) (2024)
- [Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation](https://mlsanity.com/paper/2312.05276/) (2023)
- [A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models](https://mlsanity.com/paper/2212.12799/) (2022)
- [Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training](https://mlsanity.com/paper/2202.13654/) (2022)
- [Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task](https://mlsanity.com/paper/2204.07459/) (2022)
- [Word Sense Linking: Disambiguating Outside the Sandbox](https://mlsanity.com/paper/2412.09370/) (2024)
- [Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?](https://mlsanity.com/paper/2505.16814/) (2025)
- [Convex Dataset Valuation for Post-Training](https://mlsanity.com/paper/2605.16704/) (2026)

## Abstract (arXiv)

Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are fine-tuned on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.