Massively Multilingual Transfer for NER
Table 3: The effect of the choice of monolingual word embeddings (Common Crawl and Wikipedia), and their cross-lingual mapping on NER direct transfer. Word translation accuracy, and direct transfer NER F1 are averaged over 40 languages.
['Unsup', 'crawl', 'Transl. Acc. 34', 'Dir.Transf. F1 26']
We experimented with Wiki and CommonCrawl monolingual embeddings from fastText Bojanowski et al. Each of the 41 languages is mapped to English embedding space using three methods from MUSE: 1) supervised with bilingual dictionaries; 2) seeding using identical character sequences; and 3) unsupervised training using adversarial learning Lample et al. The cross-lingual mappings are evaluated by precision at k=1. CommonCrawl doesn’t perform well in bilingual induction despite having larger text corpora, and underperforms in direct transfer NER. It is also evident that using identical character strings instead of a bilingual dictionary as the seed for learning a supervised bilingual mapping barely affects the performance. This finding also applies to few-shot learning over larger ensembles: running RaRe over 40 source languages achieves an average F1 of 77.9 when using embeddings trained with a dictionary, versus 76.9 using string identity instead. Experiments with unsupervised mappings performed substantially worse than supervised methods, and so we didn’t explore these further.