Abstract
Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models. Previous work has been limited to datasets from a specific language and a specific historical period, and it is not clear whether results generalize. It therefore remains an open problem, when historical text normalization benefits from multi-task learning. We explore the benefits of multi-task learning across 10 different datasets, representing different languages and periods. Our main finding—contrary to what has been observed for other NLP tasks—is that multi-task learning mainly works when target task data is very scarce.- Anthology ID:
- W18-3403
- Volume:
- Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 19–24
- Language:
- URL:
- https://aclanthology.org/W18-3403
- DOI:
- 10.18653/v1/W18-3403
- Cite (ACL):
- Marcel Bollmann, Anders Søgaard, and Joachim Bingel. 2018. Multi-task learning for historical text normalization: Size matters. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 19–24, Melbourne. Association for Computational Linguistics.
- Cite (Informal):
- Multi-task learning for historical text normalization: Size matters (Bollmann et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-3403.pdf
- Data
- FCE