求解词形态类比：从检索到生成
Solving morphological analogies: from retrieval to generation
AI总结：
本研究提出一种深度学习框架，结合检索式与生成式方法处理词形态类比的检测与求解任务，在多语言数据集上性能优于传统符号方法与基线模型，并给出应用指导。
摘要：
类比推理是人类推理的一项卓越能力，一直被用于解决复杂的推理任务。基于类比的推理（AR）在人工智能领域受到越来越多的关注，已在分类、决策、推荐等多项机器学习任务中展现出潜力，取得了具有竞争力的结果。我们提出一种深度学习（DL）框架，用于应对并解决AR中的两项关键任务：类比检测与类比求解。该框架在词形态类比比例（AP）数据集Siganalogies上进行了全面测试，结果显示其在多种语言上的表现均优于符号方法。此前的研究已经探索了用于分类的类比神经网络（ANNc）在类比检测中的表现，以及用于检索的类比神经网络（ANNr）在基于检索的类比求解中的表现，同时也探究了自编码器（AE）通过生成解词来完成类比求解的潜力。在本文中，我们总结了这些研究发现，并通过结合ANNr与AE嵌入模型对其进行拓展，同时检验了ANNc作为检索方法的性能。ANNr与AE的组合在几乎所有情况下的表现都优于其他方法，而作为检索方法的ANNc取得了与3CosMul相当甚至更优的性能。最后我们给出了使用本框架以深度学习方法处理类比比例问题的通用指导原则。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
