III: Small: Collaborative Research: Modeling Pre- and Post- Conditions for Understanding Events

这个项目开发了学习真实世界事件的典型前置条件和后置条件的新算法。这些逻辑条件对于开发更好的语言理解应用程序至关重要，这些应用程序可以准确地推理书面文本中描述的情况。在文本的自动理解方面已经有了重大的技术进步，但事件推理需要通常未陈述和隐含的知识。例如，如果一次会议被取消，案文中说会议原定举行(前提条件)，现在将不再举行(后置条件)，这是不寻常的。虽然这些对人类来说是显而易见的，但这些条件是未知的，对建立辅助技术至关重要。这种推理可以实现完整的文档理解，支持精确和可解释的问题回答，提高了语言生成的输出。该项目广泛应用于各种辅助技术，以帮助公众获取和了解信息。学习前提条件可以通过解释特定情况是如何发生的来进一步教育文本探索应用。更好的语言理解还有助于解释自动决策，使关键任务领域的技术更值得信赖。最后，该项目将通过培养研究生和本科生，帮助解决计算机科学和机器学习关键领域的人才短缺问题。该项目专注于开发学习前置条件关系和后置条件关系的模型，以及实现这种学习所需的大数据集。项目计划的第一个重点是开发新的条件知识数据集，然后开发初始监督学习算法来检测文本中的条件知识。该项目最初将重点放在今天的大规模语言模型上，以建立具有竞争力的基线，该项目的其余部分将在此基础上进行改进。在创建了这些带注释的数据集和基线之后，重点将转向开发生成性神经体系结构，如用丰富的结构化潜在空间增强的变分自动编码器。这些空间将增加实体网络，使其能够跟踪一般事件知识，但也包括关于实体的具体知识。生成模型的动机也是从大量未标记的文本集合中聚合条件知识。最后，除了开发大规模数据集来学习这些知识外，该项目还将开发新的推理任务，以促使社区开发更精确的语言理解模型，并利用这些任务进一步研究更丰富的事件知识模型。这项研究的所有科学发现、数据集和其他人工制品将向科学界和更广泛的公众开放。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project develops new algorithms for learning the typical pre-conditions and post-conditions of real-world events. These logical conditions are crucial for developing better language understanding applications that can reason precisely about situations described in written text. There have been significant technological advances in the automatic understanding of text, but event reasoning requires knowledge that is often unstated and implicit. For example, if a meeting is canceled, it would be unusual for the text to say that the meeting was scheduled to happen (a pre-condition), and that it will now no longer happen (a post-condition). While these are obvious to a human, these conditions are unknown and crucial to building assistive technology. This kind of reasoning can enable complete document understanding, support precise and explainable question answering, and improve the output of language generation. This project has broad applications to a variety of assistive technology for information access and understanding for the general public. Learning pre-conditions can further educational text exploration applications by explaining how a certain situation came about. Better language understanding can also help explain automated decisions, making technology more trustworthy in mission critical domains. And finally, this project will help address the shortage of talent in the critical areas of computer science and machine learning by training graduate and undergraduate students. This project focuses on developing both the models to learn pre- and post-condition relations, but also the large datasets required to enable this learning. The first thrust in the project plan is to develop new datasets of conditional knowledge, and then to develop initial supervised learning algorithms to detect them in text. The project will initially focus on today’s large-scale language models to establish competitive baselines that the rest of the project will improve upon. After creating these annotated datasets and baselines, the focus will then turn to developing generative neural architectures like variational autoencoders that are augmented with rich structured latent spaces. These spaces will be augmented with entity networks that allow it to track generic event knowledge, but also specific knowledge about the entities. The motivation for generative models is to aggregate condition knowledge from large collections of unlabeled text as well. Finally, in addition to developing large scale datasets to learn this knowledge, the project will develop new reasoning tasks that could spur the community to develop more precise language understanding models, and to use these tasks to further research into richer models of event knowledge. All scientific findings, datasets, and other artifacts of the research will be made available for the scientific community and the broader public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.