Abstract
Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner. We propose a framework in which our model begins learning syntax and translation interleaved, gradually putting more focus on translation. Using this approach, we achieve considerable improvements in terms of BLEU score on relatively large parallel corpus (WMT14 English to German) and a low-resource (WIT German to English) setup.- Anthology ID:
- Q18-1017
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 6
- Month:
- Year:
- 2018
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova, Brian Roark
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 225–240
- Language:
- URL:
- https://aclanthology.org/Q18-1017
- DOI:
- 10.1162/tacl_a_00017
- Cite (ACL):
- Eliyahu Kiperwasser and Miguel Ballesteros. 2018. Scheduled Multi-Task Learning: From Syntax to Translation. Transactions of the Association for Computational Linguistics, 6:225–240.
- Cite (Informal):
- Scheduled Multi-Task Learning: From Syntax to Translation (Kiperwasser & Ballesteros, TACL 2018)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/Q18-1017.pdf
- Data
- Penn Treebank