Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders
Table 5: SARI score of models with different noise. All models are trained in semi-supervised manner with 10% parallel corpus.
['[BOLD] Noise Type', '[BOLD] Newsela  [BOLD] SARI', '[BOLD] Newsela  [ITALIC] Fkeep', '[BOLD] Newsela  [ITALIC] Fdel', '[BOLD] Newsela  [ITALIC] Fadd', '[BOLD] WikiLarge  [BOLD] SARI', '[BOLD] WikiLarge  [ITALIC] Fkeep', '[BOLD] WikiLarge  [ITALIC] Fdel', '[BOLD] WikiLarge  [ITALIC] Fadd']
Furthermore, we analyze the insights of SARI score in detail. Additive noise in simple sentences can significantly promote the delete and add operation. Substitution also has a similar effect and makes a further improvement. Model with original noise tend to copy directly from the source sentence, resulting a relative higher F-score in keep operation, but much lower scores on other aspects.