Controlling Text Complexity in Neural Machine Translation
Table 7: Data ablation experiments showing the impact of different types of training examples on multi-task model. The OPUS parallel corpus is essential to good performance. Simplification data (S) can be used for simplification supervision when joint translation and simplification examples (MT+S) are unavailable.
['Newsela S', 'Newsela MT+S', 'Newsela MT', 'OPUS MT', 'Evaluation Metrics BLEU', 'Evaluation Metrics SARI', 'Evaluation Metrics PCC']
OPUS improves BLEU and SARI performance across the board. However, using OPUS without any Newsela MT data (Row 4) hurts the correlation score, indicating the importance of in-domain MT data to control complexity. The difference between the performance when using joint translation and simplification (MT+S) examples (Row 2) vs. simplification only (S in Row 3) is small in terms of BLEU (+0.11) and PCC (0.012), indicating that the monolingual simplification dataset can provide simplification supervision when MT+Simplify data is unavailable. The overall best performance for the task is obtained by using all types of training examples. Example translations produced by our best multitask model.