CAREER: Knowledge-Rich Neural Text Comprehension and Reasoning

大量不断变化的知识以不同的文本样式（例如，新闻与科学文本）和不同的格式（知识库与网页与文本文档）在线提供。本提案解决了文本理解和推理的问题，考虑到这种多样性：人工智能（AI）如何帮助应用程序理解和结合来自变量的证据，不断发展的文本知识来源，以做出复杂的推断并得出逻辑结论？深度学习算法、大规模数据集和工业规模计算资源的最新进展正在推动许多自然语言处理（NLP）任务的进展，包括问题回答。然而，目前的模型缺乏回答复杂问题的能力，这些问题需要它们在不同的来源中进行智能推理，并解释它们的决定。此外，当任务注释的训练数据稀缺且计算资源有限时，这些模型无法扩展。我们的研究结果将产生下一代问答和事实检查算法，即使在注释训练数据稀缺的情况下，也可以使用多跳和可解释推理提供丰富的自然语言理解。本研究将重点放在文本理解和推理上，将符号人工智能方法的能力整合到当前的深度学习算法中。它将设计混合的、可解释的算法，理解和推理不同格式和风格的文本知识，推广到训练数据稀缺的新兴领域（鲁棒性），并在资源限制下高效运行（可扩展）。为此，本研究将侧重于四个变革性研究计划：(1)定义一个通用的形式体系，通过知识丰富的神经表示来促进数据理解；(2)设计一个可解释的、多跳推理和推理引擎；(3)开发健壮的、可扩展的算法来展示可推广的领域和设备适应性；(4)在问答和事实检查任务中构建应用程序和数据集，这些应用程序和数据集将具有持久的通用实用性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Enormous amounts of ever-changing knowledge are available online in diverse textual styles (e.g., news vs. science text) and diverse formats (knowledge bases vs. web pages vs. textual documents). This proposal addresses the question of textual comprehension and reasoning given this diversity: how can artificial intelligence (AI) help applications comprehend and combine evidence from variable, evolving sources of textual knowledge to make complex inferences and draw logical conclusions? Recent advances in deep learning algorithms, large-scale datasets, and industry-scale computational resources are spurring progress in many Natural Language Processing (NLP) tasks, including question answering. Nevertheless, current models lack the ability to answer complex questions that require them to reason intelligently across diverse sources and explain their decisions. Further, these models cannot scale up when task-annotated training data are scarce and computational resources are limited. Our results will give rise to the next generation of question answering and fact checking algorithms that offer rich natural language comprehension using multi-hop and interpretable reasoning even when annotated training data is scarce. With a focus on textual comprehension and reasoning, this research will integrate capabilities of symbolic AI approaches into current deep learning algorithms. It will devise hybrid, interpretable algorithms that understand and reason about textual knowledge across varied formats and styles, generalize to emerging domains with scarce training data (are robust), and operate efficiently under resource limitations (are scalable). Toward this end, this research will focus on four transformative research initiatives: (1) defining a general-purpose formalism to promote data comprehension through knowledge-rich neural representations, (2) devising an interpretable, multi-hop inference and reasoning engine, (3) developing robust and scalable algorithms to demonstrate generalizable domain and device adaptation, and (4) building applications and datasets in question answering and fact checking tasks that will have lasting general-purpose utility.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.