论文标题
使用约束的披肩完成从语言模型中提取无监督的关系
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion
论文作者
论文摘要
我们表明，最先进的自我监督语言模型可以轻松地用于从语料库中提取关系，而无需训练微调的提取头。我们介绍了Re-Flex，这是一个简单的框架，对经过验证的语言模型执行限制的披肩完成，以执行无监督的关系提取。 ReFlex使用上下文匹配来确保语言模型预测与与目标关系相关的输入语料库的支持证据匹配。我们对多个关系提取基准进行了一项广泛的实验研究，并证明，与次要方法相比，基于预审前的语言模型的无监督关系提取方法的竞争优于无监督关系提取方法的竞争。我们的结果表明，针对语言模型的被限制的推理查询可以实现准确的无监督关系提取。
We show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a simple framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. RE-Flex uses contextual matching to ensure that language model predictions matches supporting evidence from the input corpus that is relevant to a target relation. We perform an extensive experimental study over multiple relation extraction benchmarks and demonstrate that RE-Flex outperforms competing unsupervised relation extraction methods based on pretrained language models by up to 27.8 $F_1$ points compared to the next-best method. Our results show that constrained inference queries against a language model can enable accurate unsupervised relation extraction.
