A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system. We propose a novel approach for generating explanations of a natural language interface based on semantic parsing. We focus on counterfactual explanations, which are post-hoc explanations that describe to the user how they could have minimally modified their utterance to achieve their desired goal. In particular, the user provides an utterance along with a demonstration of their desired goal; then, our algorithm synthesizes a paraphrase of their utterance that is guaranteed to achieve their goal. In two user studies, we demonstrate that our approach substantially improves user performance, and that it generates explanations that more closely match the user's intent compared to two ablations.
翻译:自然语言界面所面临的一项关键挑战是使用户能够理解基本系统的能力。 我们提出一种新的方法,根据语义区分对自然语言界面作出解释。 我们注重反事实解释,即事后解释,向用户说明他们如何能微小地修改其言论以实现其预期目标。 特别是,用户在表达其预期目标的同时,还提供一种话语; 然后,我们的算法合成了他们的语句的副词,保证它们能够实现其目标。 在两项用户研究中,我们证明我们的方法大大改进了用户的性能,并提供了更贴近用户意图的解释,而不是两种推算。