论文标题
通过从推理路径中提取桥梁概念来产生常识解释
Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths
论文作者
论文摘要
常识性解释生成旨在通过对反对常识的陈述产生合理的解释来增强机器的感知能力。尽管这项任务对人类很容易，但机器仍在努力产生合理且内容丰富的解释。在这项工作中，我们提出了一种方法，该方法首先提取基础概念，这些概念在推理链中用作\ textit {bridges}，然后集成这些概念以生成最终解释。为了促进推理过程，我们通过提取和修剪多跳路路径来构建子图。我们设计了一个桥梁概念提取模型，该模型首先得分三倍，路由子图中的路径，并进一步选择三重级别和概念级别的桥梁概念。我们对常识性解释生成任务进行实验，我们的模型在自动和人类评估中都优于最先进的基准。
Commonsense explanation generation aims to empower the machine's sense-making capability by generating plausible explanations to statements against commonsense. While this task is easy to human, the machine still struggles to generate reasonable and informative explanations. In this work, we propose a method that first extracts the underlying concepts which are served as \textit{bridges} in the reasoning chain and then integrates these concepts to generate the final explanation. To facilitate the reasoning process, we utilize external commonsense knowledge to build the connection between a statement and the bridge concepts by extracting and pruning multi-hop paths to build a subgraph. We design a bridge concept extraction model that first scores the triples, routes the paths in the subgraph, and further selects bridge concepts with weak supervision at both the triple level and the concept level. We conduct experiments on the commonsense explanation generation task and our model outperforms the state-of-the-art baselines in both automatic and human evaluation.
