论文标题
迈向广义开放信息提取
Towards Generalized Open Information Extraction
论文作者
论文摘要
开放信息提取（OpenIE）促进了文本事实的开放域发现。但是，除了培训语料库之外，普遍的解决方案在内域测试集上评估了Openie模型，这肯定违反了域独立的最初任务原理。在本文中，我们建议将Openie推向更现实的方案：概括从源培训域中具有不同数据分布的看不见的目标域，称为概括。为此，我们首先引入Globe，这是一个大规模的人类通知的多域开放式基准测试，以检查最近的OpenIE模型对域转移的鲁棒性，高达70％的相对性能降解意味着广义开放的挑战。然后，我们提出了Dragonie，探索了文本事实的极简主义图表：定向无环图，以改善开放式的概括。广泛的实验表明，Dragonie绝对在F1分数中击败了以前的方法和室外设置的先前方法，但仍然有足够的改进空间。
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts. However, the prevailing solutions evaluate OpenIE models on in-domain test sets aside from the training corpus, which certainly violates the initial task principle of domain-independence. In this paper, we propose to advance OpenIE towards a more realistic scenario: generalizing over unseen target domains with different data distributions from the source training domains, termed Generalized OpenIE. For this purpose, we first introduce GLOBE, a large-scale human-annotated multi-domain OpenIE benchmark, to examine the robustness of recent OpenIE models to domain shifts, and the relative performance degradation of up to 70% implies the challenges of generalized OpenIE. Then, we propose DragonIE, which explores a minimalist graph expression of textual fact: directed acyclic graph, to improve the OpenIE generalization. Extensive experiments demonstrate that DragonIE beats the previous methods in both in-domain and out-of-domain settings by as much as 6.0% in F1 score absolutely, but there is still ample room for improvement.
