面向关系抽取的通用和趋势感知课程学习

Nidhi Vakil, Hadi Amiri
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引用次数: 6

摘要

我们提出了一种通用的趋势感知课程学习方法,该方法有效地集成了文本图中的文本和结构信息,用于实体之间的关系提取,我们将实体视为图中的节点对。提出的模型扩展了现有的课程学习方法,通过纳入样本水平的损失趋势来更好地区分容易和困难的样本,并安排它们进行训练。该模型对样本难度进行了稳健的估计,并在多个数据集上显示出比最先进的方法有相当大的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generic and Trend-aware Curriculum Learning for Relation Extraction
We present a generic and trend-aware curriculum learning approach that effectively integrates textual and structural information in text graphs for relation extraction between entities, which we consider as node pairs in graphs. The proposed model extends existing curriculum learning approaches by incorporating sample-level loss trends to better discriminate easier from harder samples and schedule them for training. The model results in a robust estimation of sample difficulty and shows sizable improvement over the state-of-the-art approaches across several datasets.
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