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

ACL 2019  ·  Benjamin Heinzerling, Michael Strube ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Part-Of-Speech Tagging UD MultiBPEmb Avg accuracy 96.62 # 3