论文标题
在评估答案段落级别的多个问题一代
On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation
论文作者
论文摘要
我们研究了从给定段落中预测一组突出问题的任务，而没有任何确切答案的知识。我们做出了两个主要贡献。首先，我们提出了一种新方法，通过使用匈牙利算法在评分分配对之前，通过使用匈牙利算法将预测的问题分配给参考文献，以评估一组针对参考的预测问题。我们表明，与先前的方法相比，我们提出的评估策略具有更好的理论和实用性，因为它可以正确地说明参考文献的覆盖范围。其次，我们比较了使用预训练的SEQ2SEQ模型来生成和选择与给定段落有关的一组问题的不同策略。该代码可用。
We study the task of predicting a set of salient questions from a given paragraph without any prior knowledge of the precise answer. We make two main contributions. First, we propose a new method to evaluate a set of predicted questions against the set of references by using the Hungarian algorithm to assign predicted questions to references before scoring the assigned pairs. We show that our proposed evaluation strategy has better theoretical and practical properties compared to prior methods because it can properly account for the coverage of references. Second, we compare different strategies to utilize a pre-trained seq2seq model to generate and select a set of questions related to a given paragraph. The code is available.
