Controllable Sentence Simplification
Table 2: Comparison to the literature. We report the results of the model that performed the best on the validation set among all runs and parametrizations. The ratios used for parametrizations are written as subscripts.
['WikiLarge  [BOLD] (test)', 'SARI ↑', 'FKGL ↓']
Dissimilar candidates are favored based on their Levenshtein distance to the source. Hybrid Deep semantics sentence representation fed to a monolingual MT system. Seq2Seq trained with reinforcement learning, combined with a lexical simplification model. Seq2Seq model based on the pointer-copy mechanism and trained via multi-task learning on the Entailment and Paraphrase Generation tasks. The second beam search hypothesis is selected during decoding; the hypothesis number is an hyper-parameter fine-tuned with SARI. Seq2Seq with a memory-augmented Neural Semantic Encoder, tuned with SARI. DMASS+DCSS Seq2Seq The database is also used to modify the loss and re-weight word probabilities to favor simpler words.