Papers with post-editing effectiveness
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages (2022.emnlp-main)
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| Challenge: | Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages. |
| Approach: | They propose to use a dataset to assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. |
| Outcome: | The proposed model is faster than translation from scratch, but the magnitude of productivity gains varies widely across systems and languages. |