论文标题
在情况下对段落效应的可解释推理
Towards Interpretable Reasoning over Paragraph Effects in Situation
论文作者
论文摘要
我们专注于在情况下对段落效应进行推理的任务，这需要一个模型来了解背景段落中描述的因果，并将知识应用于新的情况。现有作品忽略了复杂的推理过程，并使用一个步骤的“黑匣子”模型来解决它。受到人类认知过程的启发，在本文中，我们为此任务提出了一种顺序方法，该方法将通过神经网络模块明确地对推理过程的每个步骤进行建模。特别是，以端到端的方式设计和学习了五个推理模块，这导致了更容易解释的模型。绳索数据集的实验结果证明了我们提出的方法的有效性和解释性。
We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the knowledge to a novel situation. Existing works ignore the complicated reasoning process and solve it with a one-step "black box" model. Inspired by human cognitive processes, in this paper we propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules. In particular, five reasoning modules are designed and learned in an end-to-end manner, which leads to a more interpretable model. Experimental results on the ROPES dataset demonstrate the effectiveness and explainability of our proposed approach.
