Analogical inference is a remarkable capability of human reasoning, and has been used to solve hard reasoning tasks. Analogy based reasoning (AR) has gained increasing interest from the artificial intelligence community and has shown its potential in multiple machine learning tasks such as classification, decision making and recommendation with competitive results. We propose a deep learning (DL) framework to address and tackle two key tasks in AR: analogy detection and solving. The framework is thoroughly tested on the Siganalogies dataset of morphological analogical proportions (APs) between words, and shown to outperform symbolic approaches in many languages. Previous work have explored the behavior of the Analogy Neural Network for classification (ANNc) on analogy detection and of the Analogy Neural Network for retrieval (ANNr) on analogy solving by retrieval, as well as the potential of an autoencoder (AE) for analogy solving by generating the solution word. In this article we summarize these findings and we extend them by combining ANNr and the AE embedding model, and checking the performance of ANNc as an retrieval method. The combination of ANNr and AE outperforms the other approaches in almost all cases, and ANNc as a retrieval method achieves competitive or better performance than 3CosMul. We conclude with general guidelines on using our framework to tackle APs with DL.

翻译：类比推理是人类推理的重要能力，已被用于解决困难推理任务。基于类比的推理在人工智能领域日益受到关注，并在分类、决策和推荐等机器学习任务中展现出具有竞争力的潜力。我们提出一个深度学习框架，用于处理类比推理中的两个关键任务：类比检测与求解。该框架在包含词间形态类比比例的Siganalogies数据集上进行了全面测试，结果表明其在多种语言中均优于符号方法。此前的研究已探索了分类用类比神经网络在类比检测中的行为、检索用类比神经网络通过检索求解类比的表现，以及利用自编码器通过生成目标词来求解类比的潜力。本文总结这些发现并通过以下方式加以延伸：将检索用类比神经网络与自编码器嵌入模型相结合，并检验分类用类比神经网络作为检索方法的性能。在所有情况下，检索用类比神经网络与自编码器的组合几乎均优于其他方法，而分类用类比神经网络作为检索方法时，其性能与3CosMul相当或更优。最后，我们提出关于利用本框架通过深度学习处理类比比例的通用指南。