Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and efficiency by helping users interpret documents' relevance. Existing works mostly focus on factual explanation, i.e. to find/generate supporting evidence about documents' relevance to search queries. However, research in cognitive sciences has shown that human explanations are contrastive i.e. people explain an observed event using some counterfactual events; such explanations reduce cognitive load and provide actionable insights. Though already proven effective in machine learning and NLP communities, there lacks a strict formulation on how counterfactual explanations should be defined and structured, in the context of web search. In this paper, we first discuss the possible formulation of counterfactual explanations in the IR context. Next, we formulate a suite of desiderata for counterfactual explanation in SeRE task and corresponding automatic metrics. With this desiderata, we propose a method named \textbf{C}ounter\textbf{F}actual \textbf{E}diting for Search Research \textbf{E}xplanation (\textbf{CFE2}). CFE2 provides pairwise counterfactual explanations for document pairs within a search engine result page. Our experiments on five public search datasets demonstrate that CFE2 can significantly outperform baselines in both automatic metrics and human evaluations.

翻译：搜索结果解释（SeRE）旨在通过帮助用户理解文档相关性来提高搜索会话的效能与效率。现有研究大多聚焦于事实性解释，即寻找或生成文档与搜索查询相关的支持性证据。然而，认知科学研究表明，人类的解释具有对比性——人们常通过反事实事件来解释观察到的现象；此类解释能降低认知负荷并提供可操作的见解。尽管在机器学习与自然语言处理领域已被证实有效，但在网络搜索语境中，关于反事实解释应如何定义与构建仍缺乏严格的形式化框架。本文首先探讨了信息检索语境下反事实解释的可能形式化方案。继而，我们为SeRE任务中的反事实解释构建了一套期望准则及相应的自动评估指标。基于此准则，我们提出了一种名为“面向搜索研究解释的反事实编辑”（CFE2）的方法。CFE2为搜索引擎结果页面中的文档对提供成对反事实解释。我们在五个公开搜索数据集上的实验表明，CFE2在自动评估指标与人工评估中均显著优于基线模型。