# Massively Multilingual Transfer for NER

arXiv: https://arxiv.org/abs/1902.00193 · HTML: https://mlsanity.com/paper/1902.00193/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

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

- [Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages](https://mlsanity.com/paper/2207.06882/) (2022)
- [What are the best systems? New perspectives on NLP Benchmarking](https://mlsanity.com/paper/2202.03799/) (2022)

## Abstract (arXiv)

In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a `massive' setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model.