A Multi task Learning Approach to Adapting Bilingual Word Embeddings for Cross lingual Named Entity Recognition
We show how to adapt bilingual word embeddings (BWE's) to bootstrap a cross lingual name entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine tuned for the NER task.
IJCNLP