Furthermore, a smaller vocabulary size is more convenient for model training since training time increases with vocabulary size (Morin and Bengio, 2005) and hence a model with a smaller vocabulary trains faster.
in this work, we answer this question for multilingual named entity recognition and part-of-speech tagging and make the following contributions: • We present a large-scale evaluation of multilingual subword representations on two sequence tagging tasks; • We find that subword vocabulary size matters and give recommendations for choosing it; • We find that different methods have different strengths: Monolingual BPEmb works best in medium-and high-resource settings, multilingual non-contextual subword embeddings are best in low-resource languages, while multilingual BERT gives good or best results across languages.