@inproceedings{johnson-etal-2019-cross,
    title = {Cross-lingual Transfer Learning for {J}apanese Named Entity Recognition},
    author = {Johnson, Andrew  and       Karanasou, Penny  and      Gaspers, Judith  and      Klakow, Dietrich},
    booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)},
    month = jun,
    year = {2019},
    address = {Minneapolis, Minnesota},
    publisher = {Association for Computational Linguistics},
    url = {https://www.aclweb.org/anthology/N19-2023},
    doi = {10.18653/v1/N19-2023},
    pages = {182--189},
    abstract = {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 real-world 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.},
}