  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.
