Papers with NLLB-200 models
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent years have witnessed that massively multilingual neural machine translation (MMNMT) achieves a remarkable progress in both high- and low-resource language translation. |
| Approach: | They propose to use a robustness evaluation benchmark dataset to assess the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. |
| Outcome: | The proposed dataset is publicly available at https://github.com/ID-ZH-MTRobustEval. |