  Sentence simplification aims to improve readability and understandability,
based on several operations such as splitting, deletion, and paraphrasing.
However, a valid simplified sentence should also be logically entailed by its
input sentence. In this work, we first present a strong pointer-copy mechanism
based sequence-to-sequence sentence simplification model, and then improve its
entailment and paraphrasing capabilities via multi-task learning with related
auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a
novel 'multi-level' layered soft sharing approach where each auxiliary task
shares different (higher versus lower) level layers of the sentence
simplification model, depending on the task's semantic versus lexico-syntactic
nature. We also introduce a novel multi-armed bandit based training approach
that dynamically learns how to effectively switch across tasks during
multi-task learning. Experiments on multiple popular datasets demonstrate that
our model outperforms competitive simplification systems in SARI and FKGL
automatic metrics, and human evaluation. Further, we present several ablation
analyses on alternative layer sharing methods, soft versus hard sharing,
dynamic multi-armed bandit sampling approaches, and our model's learned
entailment and paraphrasing skills.
