Sentence simplification is to rephrase a sentence into a form that is easier to read and understand while preserving its essential meaning and information. Recently, monolingual neural machine translation methods have emerged as a popular approach for this task. However, these methods often overlook the syntactic tree information of sentences, which can be crucial for effective simplification. To address this issue, we propose a syntax-guided controllable sentence simplification model that leverages graph attention networks to incorporate the syntactic information of dependency trees. Specifically, besides the sentence encoder, we propose a graph encoder that encodes dependency trees to enrich the syntactic information. Within the decoder, we introduce a syntax-augmented cross-attention that aggregates both sentence and syntax information simultaneously to the target side for simplification. We evaluate our proposed model on two benchmark datasets, showcasing that it outperforms state-of-the-art methods by a significant margin. Our proposed model underscores the significance of incorporating syntactic knowledge in sentence simplification.

中文翻译：

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句法引导的可控句子简化

句子简化是将句子改写成更容易阅读和理解的形式，同时保留其基本含义和信息。最近，单语言神经机器翻译方法已成为此任务的流行方法。然而，这些方法常常忽略句子的句法树信息，这对于有效简化至关重要。为了解决这个问题，我们提出了一种语法引导的可控句子简化模型，该模型利用图注意网络来合并依存树的语法信息。具体来说，除了句子编码器之外，我们还提出了一种图编码器，可以对依存树进行编码以丰富句法信息。在解码器中，我们引入了语法增强的交叉注意，它将句子和语法信息同时聚合到目标端以进行简化。我们在两个基准数据集上评估了我们提出的模型，表明它明显优于最先进的方法。我们提出的模型强调了在句子简化中结合句法知识的重要性。