回答前区分：生成对比解释作为常识问答的知识
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering
AI总结：
提出CPACE模型，将检索的符号知识转化为对比解释以增强常识问答，在CSQA、QASC和OBQA数据集上取得SOTA或显著提升。
摘要：
现有的知识增强方法通过从不同知识库获取多样化知识，在某些问答任务中取得了显著成果。然而，受限于检索知识的属性，它们仍难以同时受益于知识相关性和区分度。为应对该挑战，我们提出了CPACE（以概念为中心的基于提示的对比解释生成模型），旨在将获取的符号知识转化为对比解释，以更好地区分给定候选项之间的差异。首先，遵循先前工作，我们通过以概念为中心的知识提取模块检索不同类型的符号知识。随后，我们将获取的符号知识和解释提示作为指导，生成相应的对比解释，以更好地建模知识区分度与可解释性。最后，我们将生成的对比解释作为外部知识，用于增强下游任务。我们在三个广泛使用的问答数据集CSQA、QASC和OBQA上进行了系列实验。实验结果表明，在生成的对比解释的帮助下，我们的CPACE模型在CSQA上实现了新的SOTA（测试集上达89.8%，比人类表现高0.9%），并在QASC和OBQA上取得了显著提升（分别为4.2%和3.5%）。
英文摘要：
Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases. However, limited by the properties of retrieved knowledge, they still have trouble benefiting from both the knowledge relevance and distinguishment simultaneously. To address the challenge, we propose CPACE, a Concept-centric Prompt-bAsed Contrastive Explanation Generation model, which aims to convert obtained symbolic knowledge into a contrastive explanation for better distinguishing the differences among given candidates. Firstly, following previous works, we retrieve different types of symbolic knowledge with a concept-centric knowledge extraction module. After that, we generate corresponding contrastive explanations using acquired symbolic knowledge and explanation prompts as guidance for better modeling the knowledge distinguishment and interpretability. Finally, we regard the generated contrastive explanation as external knowledge for downstream task enhancement. We conduct a series of experiments on three widely-used question-answering datasets: CSQA, QASC, and OBQA. Experimental results demonstrate that with the help of generated contrastive explanation, our CPACE model achieves new SOTA on CSQA (89.8% on the testing set, 0.9% higher than human performance), and gains impressive improvement on QASC and OBQA (4.2% and 3.5%, respectively).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
