Papers by Chris Mattmann
Many-to-English Machine Translation Tools, Data, and Pretrained Models (2021.acl-demo)
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| Challenge: | Commercial translation systems support only one hundred languages or fewer . commercial translation systems do not make these models available for transfer to low resource languages . |
| Approach: | They propose a multilingual neural machine translation model that can translate from 500 source languages to English. |
| Outcome: | The proposed model can translate from 500 source languages to English, or be used as a parent model for low-resource languages. |