论文标题
自动编码语言模型基于常识验证和解释的集合学习
Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation
论文作者
论文摘要
人工智能的最终目标是构建可以理解人类语言的计算机系统。了解对文本中表达的世界的常识知识是创建这种智能系统的基础和挑战性的问题之一。作为朝着这个目标迈出的一步，我们在本文Almen中提出了一种自动编码语言模型的集合学习方法，用于常识验证和解释。通过结合几种具有暹罗神经网络在内的几种先进的预训练的语言模型，包括罗伯塔，迪伯塔和伊莱特拉，我们的方法可以区分反对常识性（验证子任务）的自然语言陈述，并正确确定了反对常识的原因（说明选择选项子任务）。 Semeval-2020任务的基准数据集上的实验结果4表明，我们的方法优于最先进的模型，在验证和解释选择子任务中分别达到97.9％和95.4％的精度。
An ultimate goal of artificial intelligence is to build computer systems that can understand human languages. Understanding commonsense knowledge about the world expressed in text is one of the foundational and challenging problems to create such intelligent systems. As a step towards this goal, we present in this paper ALMEn, an Autoencoding Language Model based Ensemble learning method for commonsense validation and explanation. By ensembling several advanced pre-trained language models including RoBERTa, DeBERTa, and ELECTRA with Siamese neural networks, our method can distinguish natural language statements that are against commonsense (validation subtask) and correctly identify the reason for making against commonsense (explanation selection subtask). Experimental results on the benchmark dataset of SemEval-2020 Task 4 show that our method outperforms state-of-the-art models, reaching 97.9% and 95.4% accuracies on the validation and explanation selection subtasks, respectively.
