Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.
翻译:分句是一个重要的简化操作器。 我们在这里展示了基于自动语义解析器的简单高效的分解算法。 分解后, 文本可以进一步进行细微的简化操作。 特别是, 我们证明神经机器翻译可以在此情况下有效使用。 先前应用机器翻译进行简化, 其缺点相当严重, 因为它们过于保守, 往往无法以任何方式修改来源 。 以语义解析法为基础进行分解, 如此处所提议, 缓解了这一问题 。 广泛的自动和人文评估表明, 拟议的方法在综合词汇和结构简化方面优于最先进的方法 。