# Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation

## TL;DR

This paper systematically evaluates multilingual subword embeddings, including contextual BERT and non-contextual FastText and BPEmb, across various languages and NLP tasks, revealing their strengths and limitations.

## Contribution

It provides a comprehensive comparison of different subword embedding methods for multilingual NLP, guiding practitioners in choosing appropriate models.

## Key findings

- BERT, BPEmb, and character embeddings combined perform best overall.
- BERT excels in medium- to high-resource languages.
- Non-contextual embeddings outperform BERT in low-resource settings.

## Abstract

Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack of systematic evaluations makes it difficult for practitioners to choose between them. In this work, we conduct an extensive evaluation comparing non-contextual subword embeddings, namely FastText and BPEmb, and a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. We find that overall, a combination of BERT, BPEmb, and character representations works best across languages and tasks. A more detailed analysis reveals different strengths and weaknesses: Multilingual BERT performs well in medium- to high-resource languages, but is outperformed by non-contextual subword embeddings in a low-resource setting.