Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders
Table 3: Results on Newsela and Wiki-Large dataset
['[BOLD] WikiLarge', 'SARI', 'Component of SARI  [ITALIC] Fkeep', 'Component of SARI  [ITALIC] Fdel', 'Component of SARI  [ITALIC] Fadd', 'BLEU']
For unsupervised systems, our model outperforms previous unsupervised baselines on both datasets. Compared to LIGHT-LS and UNTS, our model achieves a large improvement (+9.28, +3.68 on SARI) on Newsela dataset. On Wiki-Large dataset, our model still outperforms the LIGHT-LS and gets similar results with UNTS on SARI, but achieves higher BLEU score. This means our model can generate more fluently and the outputs are more relevant to the source sentences. Furthermore, our unsupervised models perform closely to the state-of-the-art supervised systems. The results also show that reinforcement training is helpful to unsupervised systems. It brings 0.62 SARI improvement on Newsela and 0.08 on Wiki-Large corpus. On Newsela dataset, our model gets comparable results with DRESS and substantially outperforms Hybrid and fully supervised sequence-to-sequence model. We have similar results on Wiki-Large dataset and our model achieves the highest score on adequacy.