Research on Open Information Extraction (Open IE) has made great progress in recent years; it is the task that detects a group of structured, machine-readable statements usually represented in triple form or n-ary relation statements. Open IE is among the core areas of the territory of Natural Language Processing (NLP), and these extractions decompose grammatically complex sentences in a corpus into the relationships they represent, which can be leveraged for various downstream tasks. Even though a lot of work has been done in this direction, there are still many issues with the existing strategies. Most of the previous Open IE systems employ a group of artificially constructed patterns to detect and extract relational tuples from a sentence in a corpus, and these patterns are either automatically learned from annotated training examples or hand-crafted. Such an approach faces some issues, the first is that it requires a lot of manpower. Secondly, they used many NLP tools, therefore, error accumulation in the procedure can negatively impact the results. In this paper, we propose an Open IE approach based on the Transformer architecture. To verify our approach, we make a study using a large and public benchmark dataset, and the experimental results showed that our model achieves a better performance than many existing baselines.

近年来，开放信息提取（Open IE）的研究取得了长足的进步。它是检测一组通常以三重形式或n元关系语句表示的结构化，机器可读语句的任务。开放式IE是自然语言处理（NLP）领域的核心领域，这些提取将语料库中语法上复杂的句子分解为它们表示的关系，可将其用于各种下游任务。尽管在这个方向上已经做了很多工作，但是现有策略仍然存在许多问题。大多数以前的Open IE系统都采用一组人工构建的模式来从语料库中的句子中检测和提取相关元组，这些模式可以从带注释的培训示例中自动学习，也可以手工制作。这种方法面临一些问题，首先是它需要大量的人力。其次，他们使用了许多NLP工具，因此，过程中的错误累积会对结果产生负面影响。在本文中，我们提出了一种基于Transformer体系结构的Open IE方法。为了验证我们的方法，我们使用了一个大型的公共基准数据集进行了研究，实验结果表明，与许多现有基准相比，我们的模型具有更好的性能。我们提出了一种基于Transformer架构的开放式IE方法。为了验证我们的方法，我们使用了一个大型的公共基准数据集进行了研究，实验结果表明，与许多现有基准相比，我们的模型具有更好的性能。我们提出了一种基于Transformer架构的开放式IE方法。为了验证我们的方法，我们使用了一个大型的公共基准数据集进行了研究，实验结果表明，与许多现有基准相比，我们的模型具有更好的性能。