To explain the predicted answers and evaluate the reasoning abilities of
models, several studies have utilized underlying reasoning (UR) tasks in
multi-hop question answering (QA) datasets. However, it remains an open
question as to how effective UR tasks are for the QA task when training models
on both tasks in an end-to-end manner. In this study, we address this question
by analyzing the effectiveness of UR tasks (including both sentence-level and
entity-level tasks) in three aspects: (1) QA performance, (2) reasoning
shortcuts, and (3) robustness. While the previous models have not been
explicitly trained on an entity-level reasoning prediction task, we build a
multi-task model that performs three tasks together: sentence-level supporting
facts prediction, entity-level reasoning prediction, and answer prediction.
Experimental results on 2WikiMultiHopQA and HotpotQA-small datasets reveal that
(1) UR tasks can improve QA performance. Using four debiased datasets that are
newly created, we demonstrate that (2) UR tasks are helpful in preventing
reasoning shortcuts in the multi-hop QA task. However, we find that (3) UR
tasks do not contribute to improving the robustness of the model on adversarial
questions, such as sub-questions and inverted questions. We encourage future
studies to investigate the effectiveness of entity-level reasoning in the form
of natural language questions (e.g., sub-question forms).

通过分析采用底层推理任务（包括句子级和实体级）对三个方面的影响（1. 问答性能，2. 推理捷径， 3. 鲁棒性），我们建立了一个多任务模型来执行三个任务：句子级支撑事实预测，实体级推理预测和答案预测，实验结果表明底层推理任务可以提高问题回答的性能和防止推理捷径，但是却不能提高对小问题和反问题等对抗性问题的鲁棒性。