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.

本文提出了一个深度学习框架来解决类比检测和解决两个关键问题，并在 morphological analogical proportions 数据集上进行了测试表明优于符号方法。文章总结了之前的研究，并结合了 ANNe 和 AE 嵌入模型。最终得出该模型在绝大多数情况下都优于其他方法，并提供了使用 DL 来处理类比问题的一般指南。