EmpLite：一个轻量级的短文本重点选择序列标注模型
EmpLite: A Lightweight Sequence Labeling Model for Emphasis Selection of Short Texts
摘要：
文本内容中的单词强调旨在通过改变大小、颜色、字体、样式（粗体、斜体等），和其他印刷特征。强调的词在吸引读者注意作者希望强调的特定信息方面非常有帮助。然而，使用用于社交媒体交互的软键盘来执行这种强调是耗时的并且具有相关联的学习曲线。在本文中，我们提出了一种新的方法来自动化的重点词检测短的书面文本。据我们所知，这项工作提出了第一个轻量级的深度学习方法，用于智能手机部署的重点选择。实验结果表明，我们的方法实现了可比的准确性在一个更低的模型大小比现有的模型。我们最好的轻量级模型的内存占用量为2.82 MB，在SemEval-2020公共基准数据集上的匹配得分为0.716。
英文摘要：
Word emphasis in textual content aims at conveying the desired intention by changing the size, color, typeface, style (bold, italic, etc.), and other typographical features. The emphasized words are extremely helpful in drawing the readers' attention to specific information that the authors wish to emphasize. However, performing such emphasis using a soft keyboard for social media interactions is time-consuming and has an associated learning curve. In this paper, we propose a novel approach to automate the emphasis word detection on short written texts. To the best of our knowledge, this work presents the first lightweight deep learning approach for smartphone deployment of emphasis selection. Experimental results show that our approach achieves comparable accuracy at a much lower model size than existing models. Our best lightweight model has a memory footprint of 2.82 MB with a matching score of 0.716 on SemEval-2020 public benchmark dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
