论文标题
深切的细心排名网络学习订购句子
Deep Attentive Ranking Networks for Learning to Order Sentences
论文作者
论文摘要
我们提出了一个基于注意力的排名框架，用于在段落中学习订购句子。我们的框架建立在双向句子编码器和基于自我注意的变压器网络的基础上，以获得段落的输入顺序不变表示。此外，它允许使用各种基于排名的损失功能（例如PointSise，成对和列表等级）进行无缝训练。我们将框架应用于两个任务：句子排序和订单歧视。在各种评估指标上，我们的框架在这些任务上的表现优于各种最新方法。我们还表明，在使用成对和列表排名损失的情况下，而不是排名损失时，它可以实现更好的结果，这表明将两个或多个句子的相对位置纳入损失函数中的相对位置有助于更好的学习。
We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.
