Massively Multilingual Transfer for NER
This work addresses cross-lingual transfer challenges for low-resource languages in NLP, offering incremental improvements over existing methods.
The paper tackles the problem of poor cross-lingual transfer in named entity recognition when using many source languages, particularly from distant ones, by proposing two techniques for modulating transfer in zero-shot or few-shot settings; the results show these techniques are much more effective than strong baselines, with the unsupervised method rivaling oracle selection of the best individual model.
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.