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.

本文提出了一种基于概率领域知识构建分布式约束的框架，并且通过 Lagrangian Relaxation 解决约束的推理问题，用于端到端的事件时间关系提取任务，实验结果显示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显著地提高基线的神经网络模型。