论文标题
域知识授权端到端事件的结构性神经网暂时提取
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction
论文作者
论文摘要
提取事件临时关系是信息提取的关键任务，并且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改善任务的性能。但是，这些系统通常会遭受两个短暂的影响：1）在基于神经模型的最大后验推理（MAP）推理时，以前的系统仅使用假定是绝对正确的结构化知识，即硬约束； 2）培训有限的数据训练时，对主要时间关系的预测有偏见。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层神经网络。我们通过拉格朗日放松解决了约束的推理问题，并将其应用于端到端事件的时间关系提取任务。实验结果表明，我们的框架能够改善基线神经网络模型，在新闻和临床领域的两个广泛使用的数据集上具有强大的统计学意义。
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.
