Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them.

翻译：类比比例是用于人工智能和自然语言处理(NLP)中若干推理和分类任务的“A是B,C是D”形式的说明。例如,对语义学和形态学有类比法,事实上,为解答或探测字符字符串之间的相似性制定了象征性的方法,例如,不言而喻的方法,以及基于科尔莫戈罗夫复杂程度的类似性。在本文件中,我们提出了一种深层次的学习方法,以探测形态类比,例如,与回流或共变。我们介绍了经验结果,表明我们的框架与上述艺术象征性方法相比具有竞争力。我们还从经验上探索其跨语言的可转移能力,这凸显了它们之间的令人感兴趣的相似性。