论文标题
我知道您问的是：图形路径学习使用AMR进行常识推理
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
论文作者
论文摘要
CommonSenseQA是一项任务，其中通过具有预定义知识的常识性推理来预测正确的答案。以前的大多数作品旨在通过分布式代表来提高性能，而无需考虑从问题的语义表示中预测答案的过程。为了阐明问题的语义解释，我们提出了一个AMR-Conceptnet-prouned（ACP）图。 ACP图是从一个完整的集成图中修剪的，其中包含从输入问题和外部常识性知识图，概念网（CN）生成的抽象含义表示（AMR）图。然后利用ACP图来解释推理路径，并预测CommonSenseQA任务的正确答案。本文介绍了通过ACP图提供的关系和概念来解释常识性推理过程的方式。此外，基于ACP的模型显示出胜过基线的模型。
CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.
