Scientific claim verification can help the researchers to easily find the
target scientific papers with the sentence evidence from a large corpus for the
given claim. Some existing works propose pipeline models on the three tasks of
abstract retrieval, rationale selection and stance prediction. Such works have
the problems of error propagation among the modules in the pipeline and lack of
sharing valuable information among modules. We thus propose an approach, named
as ARSJoint, that jointly learns the modules for the three tasks with a machine
reading comprehension framework by including claim information. In addition, we
enhance the information exchanges and constraints among tasks by proposing a
regularization term between the sentence attention scores of abstract retrieval
and the estimated outputs of rational selection. The experimental results on
the benchmark dataset SciFact show that our approach outperforms the existing
works.

提出了一种基于机器阅读理解框架的 ARSJoint 方法，该方法可以联合学习三个任务模块，包括论文摘要检索、理由选择和立场预测，并通过提出在摘要检索的句子注意力分数和理由选择的预测输出之间的正则化项增强任务之间的信息互换和约束，实验结果在 SciFact 数据集上表现优异。