论文标题
作为间接监督关系提取的摘要
Summarization as Indirect Supervision for Relation Extraction
论文作者
论文摘要
关系提取（RE）模型因其依赖昂贵注释的培训数据而受到质疑。考虑到摘要任务旨在从较长的上下文中获取概要信息的简洁表达，这些任务自然而然地与RE的目的一致，即提取了一种描述实体关系关系的概要信息。我们确定，将RE转换为摘要公式。肯定会根据摘要任务的间接监督导致更精确和资源有效的RE。为了实现这一目标，我们开发了句子和关系转换技术，从本质上讲是弥合摘要和重新任务的制定。我们还将约束解码技术与Trie评分结合在一起，以强大的推断进一步增强基于摘要的RE。在三个RE数据集上进行的实验证明了在全数据库和低资源设置中确保的有效性，表明摘要是改善RE模型的间接监督的有希望的来源。
Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i.e., extracting a kind of synoptical information that describes the relation of entity mentions. We present SuRE, which converts RE into a summarization formulation. SuRE leads to more precise and resource-efficient RE based on indirect supervision from summarization tasks. To achieve this goal, we develop sentence and relation conversion techniques that essentially bridge the formulation of summarization and RE tasks. We also incorporate constraint decoding techniques with Trie scoring to further enhance summarization-based RE with robust inference. Experiments on three RE datasets demonstrate the effectiveness of SuRE in both full-dataset and low-resource settings, showing that summarization is a promising source of indirect supervision to improve RE models.
