Tag-less Back-Translation
| Autor: | Bashir Shehu Galadanci, Idris Abdulmumin, Aliyu Garba |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Linguistics and Language Computer Science - Machine Learning Machine translation Computer science Low resource Computer Science - Artificial Intelligence Translation (geometry) computer.software_genre Language and Linguistics Machine Learning (cs.LG) Artificial Intelligence Effective method Adaptation (computer science) Computer Science - Computation and Language business.industry Back translation A domain Artificial Intelligence (cs.AI) Artificial intelligence Computational linguistics business computer Computation and Language (cs.CL) Natural language processing Software |
| DOI: | 10.48550/arxiv.1912.10514 |
| Popis: | An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of the back-translations of the target-side monolingual data. The standard back-translation method has been shown to be unable to efficiently utilize the available huge amount of existing monolingual data because of the inability of translation models to differentiate between the authentic and synthetic parallel data during training. Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and authentic data, improving standard back-translation and also enabling the use of iterative back-translation on language pairs that underperformed using standard back-translation. In this work, we approach back-translation as a domain adaptation problem, eliminating the need for explicit tagging. In the approach -- \emph{tag-less back-translation} -- the synthetic and authentic parallel data are treated as out-of-domain and in-domain data respectively and, through pre-training and fine-tuning, the translation model is shown to be able to learn more efficiently from them during training. Experimental results have shown that the approach outperforms the standard and tagged back-translation approaches on low resource English-Vietnamese and English-German neural machine translation. Comment: 29 pages, 4 figures, 13 tables |
| Databáze: | OpenAIRE |
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