Papers with IWSLT2012 datasets
Triangular Architecture for Rare Language Translation (P18-1)
Copied to clipboard
| Challenge: | Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language. |
| Approach: | They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs. |
| Outcome: | Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods. |