Abstract
Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper and we propose two simple search based curricula – orderings of the multilingual training data – which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally and we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system using contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.- Anthology ID:
- 2021.mtsummit-research.1
- Volume:
- Proceedings of Machine Translation Summit XVIII: Research Track
- Month:
- August
- Year:
- 2021
- Address:
- Virtual
- Venue:
- MTSummit
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 1–9
- Language:
- URL:
- https://aclanthology.org/2021.mtsummit-research.1
- DOI:
- Cite (ACL):
- Gaurav Kumar, Philipp Koehn, and Sanjeev Khudanpur. 2021. Learning Curricula for Multilingual Neural Machine Translation Training. In Proceedings of Machine Translation Summit XVIII: Research Track, pages 1–9, Virtual. Association for Machine Translation in the Americas.
- Cite (Informal):
- Learning Curricula for Multilingual Neural Machine Translation Training (Kumar et al., MTSummit 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.mtsummit-research.1.pdf
- Data
- FLoRes