Controlling Text Complexity in Neural Machine Translation
Table 4: Compared to pipeline models, multitask models produce complexity controlled translations that better match human references (BLEU), that are simpler (SARI), and whose resulting complexity correlates better with the target grade level (PCC). Pipeline models are trained on Newsela Simplification data and MT parallel data from Newsela and OPUS. “Translate and Simplify” uses the exact same data in a multi-task model. The “All tasks” model uses all data available, including Newsla MT+Simplify examples.
['Complexity cont. MT', 'BLEU', 'SARI', 'PCC']
Overall, results show that compared to pipeline models, multitask models produce complexity controlled translations that better match human references according to BLEU. SARI suggests that multitask translations are simpler than baseline translations, and their resulting complexity correlates better with reference grade levels according to PCC.