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.
翻译:在这项工作中,我们首先提出一个强大的指针-复制机制,基于顺序到顺序的句子简化模式,然后通过多任务学习,通过相关的附带任务和引言生成,改进它的内涵和分解能力。此外,我们提议一种新型的“多层次”分层软共享办法,其中每个辅助任务根据任务语义简化模式的不同层(高层次和低层次)共享(高层次),这取决于任务语义简化模式的语义简化性质。我们还采用了一种新的多条纹式培训办法,动态地学习如何在多任务学习期间有效地在不同任务之间转换。对多种流行数据集的实验表明,我们的模式在SARI和FKGL自动度和人文评估中超越了竞争性简化系统。此外,我们提出了关于替代层共享方法、软式与硬式共享、动态多条形抽样方法和学习的技能等的几种对比分析。