论文标题
何时使用的内容：对下游应用的开放系统的深入比较经验分析
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications
论文作者
论文摘要
开放信息提取（OpenIE）已用于各种NLP任务的管道中。不幸的是，在哪些任务中尚无明确共识。进一步的事情是缺乏考虑不同训练集的比较。在本文中，我们介绍了针对申请的神经开放模型，培训集和基准测试的经验调查，以帮助用户选择最合适的开放系统。我们发现，不同模型和数据集做出的不同假设对性能具有统计学上的显着影响，这使得为应用程序的应用程序选择最合适的模型很重要。我们在下游复杂的QA应用程序上演示了建议的适用性。
Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one's applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application.
