Controllable Text Simplification with Explicit Paraphrasing
Controllable Text Simplification with Explicit Paraphrasing
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DOI：
10.18653/v1/2021.naacl-main.277
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Mounica Maddela;Fernando Alva-Manchego;W. Xu
中科院分区：
文献类型：
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作者：
Mounica Maddela;Fernando Alva-Manchego;W. Xu
文本简化通过几种重写转换来提高句子的可读性，例如词汇释义、删除和拆分。目前的简化系统主要是端到端训练的序列到序列模型，以同时执行所有这些操作。然而，这样的系统仅限于删除单词，不能轻易适应不同目标受众的要求。在本文中，我们提出了一种新的混合方法，该方法利用语言驱动的规则来进行拆分和删除，并将它们与神经释义模型相结合来产生不同的重写风格。我们引入了一种新的数据扩充方法来提高模型的释义能力。通过自动和手动评估，我们的模型为任务建立了一个新的最先进的状态，比现有的系统更频繁地转述，并且可以控制应用于输入文本的每次简化操作的程度。
Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts.