Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation
| Autor: | Kiran Kiani, Ammara Zafar, Raheel Nawaz, Sadaf Abdul Rauf, Noor-e Hira |
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| Rok vydání: | 2019 |
| Předmět: |
Machine translation
Computer science business.industry media_common.quotation_subject computer.software_genre Translation (geometry) Domain (software engineering) Task (project management) Tokenization (data security) Quality (business) Artificial intelligence Transfer of learning business computer Natural language processing Data selection media_common |
| Zdroj: | WMT (3) |
| DOI: | 10.18653/v1/w19-5419 |
| Popis: | Transfer Learning and Selective data training are two of the many approaches being extensively investigated to improve the quality of Neural Machine Translation systems. This paper presents a series of experiments by applying transfer learning and selective data training for participation in the Bio-medical shared task of WMT19. We have used Information Retrieval to selectively choose related sentences from out-of-domain data and used them as additional training data using transfer learning. We also report the effect of tokenization on translation model performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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