Analogies are 4-ary relations of the form "A is to B as C is to D". While focus has been mostly on how to solve an analogy, i.e. how to find correct values of D given A, B and C, less attention has been drawn on whether solving such an analogy was actually feasible. In this paper, we propose a quantification of the transferability of a source case (A and B) to solve a target problem C. This quantification is based on a complexity minimization principle which has been demonstrated to be efficient for solving analogies. We illustrate these notions on morphological analogies and show its connections with machine learning, and in particular with Unsupervised Domain Adaptation.

翻译：“A是B,C是D”形式的4个类比关系是“A是B,C是B”形式的4个类比关系。虽然重点主要放在如何解决类比上,即如何找到D给A、B和C的正确值,但较少注意解决这种类比是否实际可行。在本文中,我们建议对来源案例(A和B)的可转让性进行量化,以解决目标问题C。这一量化基于一项复杂性最小化原则,已证明该原则对于解决类比是有效的。我们举例说明了这些关于形态类比的概念,并展示了它与机器学习的联系,特别是与无人监督的域适应的联系。