Reinforcement Learning based Curriculum Optimization for Neural Machine Translation

Gaurav Kumar, George Foster, Colin Cherry, Maxim Krikun


Abstract
We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, in the course of a single training run. We show that this approach can beat uniform baselines on Paracrawl and WMT English-to-French datasets by +3.4 and +1.3 BLEU respectively. Additionally, we match the performance of strong filtering baselines and hand-designed, state-of-the-art curricula.
Anthology ID:
N19-1208
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2054–2061
Language:
URL:
https://aclanthology.org/N19-1208
DOI:
10.18653/v1/N19-1208
Bibkey:
Cite (ACL):
Gaurav Kumar, George Foster, Colin Cherry, and Maxim Krikun. 2019. Reinforcement Learning based Curriculum Optimization for Neural Machine Translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2054–2061, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation (Kumar et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1208.pdf
Video:
 https://vimeo.com/355798547