The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Meta-Learning for Low-Resource Neural Machine Translation
Open accessAbstract
In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn et al., 2017) for lowresource neural machine translation (NMT).We frame low-resource translation as a metalearning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks.We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages.We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr and Ko) as target tasks.We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples.For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT'16 by seeing only 16,000 translated words (⇠ 600 parallel sentences).
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Metadata
- Authors
- Gu, J., Wang, Y., Chen, Y., Li, V. O. K., & Cho, K.
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 3622-3631
- OpenAlex
- W2888541716
- Cited by
- 325
- References
- 35
- FWCI
- 20.91
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Gu, J., Wang, Y., Chen, Y., Li, V. O. K., & Cho, K. (2018). Meta-Learning for Low-Resource Neural Machine Translation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d18-1398
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