Neural name tagging for low-resource languages

Abstract

At last, we investigate training LL name taggers without using any LL annotation. We transfer a name tagger that trained on HL annotations to a LL name tagger via two unsupervised approaches: 1) cross-lingual word embedding where we align monolingual word embedding of HL and LL into a shared space, and 2) cross-lingual language model where instead of aligning word embedding, we project the contextualized word embedding (language model) of HL and LL into a shared space.