Cross-lingual Transfer Learning for Japanese Named Entity Recognition
Andrew Johnson, Penny Karanasou, Judith Gaspers, Dietrich Klakow
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.- Anthology ID:
- N19-2023
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Anastassia Loukina, Michelle Morales, Rohit Kumar
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 182–189
- Language:
- URL:
- https://aclanthology.org/N19-2023
- DOI:
- 10.18653/v1/N19-2023
- Cite (ACL):
- Andrew Johnson, Penny Karanasou, Judith Gaspers, and Dietrich Klakow. 2019. Cross-lingual Transfer Learning for Japanese Named Entity Recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 182–189, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Transfer Learning for Japanese Named Entity Recognition (Johnson et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N19-2023.pdf
- Data
- CoNLL 2003