This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English. A deep neural network model is adopted and the best combination of weights to transfer is extensively investigated. Moreover, a novel approach is presented that overcomes linguistic differences between this language pair by romanizing a portion of the Japanese input. Experiments are conducted on external datasets, as well as internal large-scale realworld ones. Gains with TL are achieved for all evaluated cases. Finally, the influence on TL of the target dataset size and of the target tagset distribution is further investigated.
For example, REF proposed crosslingual transfer learning for NER focusing on bootstrapping Japanese from English, which has a different character set than Japanese.
Cross-lingual Transfer Learning for Japanese Named Entity Recognition
NAACL-HLT
Computer Science