Extracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding. Prior systems leverage deep learning and pre-trained language models to improve the performance of the task. However, these systems often suffer from two short-comings: 1) when performing maximum a posteriori (MAP) inference based on neural models, previous systems only used structured knowledge that are assumed to be absolutely correct, i.e., hard constraints; 2) biased predictions on dominant temporal relations when training with a limited amount of data. To address these issues, we propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge. We solve the constrained inference problem via Lagrangian Relaxation and apply it on end-to-end event temporal relation extraction tasks. Experimental results show our framework is able to improve the baseline neural network models with strong statistical significance on two widely used datasets in news and clinical domains.

中文翻译：

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用于端到端事件时间关系提取的领域知识授权结构化神经网络

提取事件时间关系是信息提取的关键任务，在自然语言理解中起着重要作用。先前的系统利用深度学习和预训练的语言模型来提高任务的性能。然而，这些系统通常存在两个缺点：1) 在基于神经模型执行最大后验 (MAP) 推理时，以前的系统仅使用假定绝对正确的结构化知识，即硬约束；2）当用有限的数据量训练时，对主要时间关系的有偏见的预测。为了解决这些问题，我们提出了一个框架，该框架通过概率领域知识构建的分布约束来增强深度神经网络。我们通过拉格朗日松弛解决约束推理问题并将其应用于端到端事件时间关系提取任务。实验结果表明，我们的框架能够在新闻和临床领域的两个广泛使用的数据集上改进具有很强统计意义的基线神经网络模型。