论文标题
词汇复杂性控制句子
Lexical Complexity Controlled Sentence Generation
论文作者
论文摘要
文本生成很少考虑对词汇复杂性的控制，这限制了其更全面的实际应用。我们介绍了一项新的词汇复杂性控制句子生成的任务，该任务旨在关键字旨在具有所需复杂性级别的句子生成。它在等级阅读，语言教学和获取等领域具有巨大的潜力。该任务的挑战是仅使用给定复杂性级别的单词生成流利的句子。我们根据复杂性嵌入为此任务提出了一种简单但有效的方法。与潜在解决方案相比，我们的方法将一词复杂性水平的表示形式融合到模型中，以更好地控制词汇复杂性。我们证明了该方法对从头开始的训练模型和对预训练模型进行微调的可行性。为了促进研究，我们分别开发了两个英语和中文数据集，并在其中进行了广泛的实验。结果表明，我们的方法可以更好地控制词汇复杂性并产生比基线方法更高的质量句子。
Text generation rarely considers the control of lexical complexity, which limits its more comprehensive practical application. We introduce a novel task of lexical complexity controlled sentence generation, which aims at keywords to sentence generation with desired complexity levels. It has enormous potential in domains such as grade reading, language teaching and acquisition. The challenge of this task is to generate fluent sentences only using the words of given complexity levels. We propose a simple but effective approach for this task based on complexity embedding. Compared with potential solutions, our approach fuses the representations of the word complexity levels into the model to get better control of lexical complexity. And we demonstrate the feasibility of the approach for both training models from scratch and fine-tuning the pre-trained models. To facilitate the research, we develop two datasets in English and Chinese respectively, on which extensive experiments are conducted. Results show that our approach better controls lexical complexity and generates higher quality sentences than baseline methods.
