RI: Small: Applying discrete reasoning steps in solving natural language processing tasks

现代自然语言处理系统可以有效地对非结构化文本数据进行浅层分析，执行诸如发现事件、识别这些事件的参与者以及将具有相同参与者的事件分组等任务。神经网络有助于使这些系统对释义等效果具有鲁棒性，但仍然主要捕获肤浅的文本模式。为了回答更深层次的问题，比如文本中事件之间的因果关系，系统可能需要结合几条信息，抽象出不相关的细节，并结合先前的世界知识来得出答案。该项目旨在开发能够解决这些挑战的系统：这些系统明确地对文本推理进行建模，并利用神经网络的力量以细致入微的方式进行推理。这种推理是通过“手把手”监督明确传授给系统的，这鼓励系统模仿人类解决问题的方式，并帮助它们更好地概括新的问题实例。这种与人类行为的一致性也有助于揭示系统的决策过程；它为他们的行为提供了一种解释形式，以便人们可以根据期望的标准（如公平）来评估他们。本文的技术创新主要集中在两个方面：设计潜在变量模型和在模型训练中开发新型的手持监督。这些技术是在需要复杂推理的三个具有挑战性的问题的背景下探索的：(1)解决数学单词问题；(2)利用世界知识解决共指问题；(3)回答文件中的问题。对于每个问题，都提出了围绕答案离散衍生的新模型，这些模型利用最先进的工具，如基于注意力的循环神经网络，来捕捉推理过程的更大背景。模型决策的离散性为纳入辅助监督提供了一个锚点，这在完全端到端神经模型中很难做到。手扶监督的性质取决于任务，是偶然监督、启发式识别衍生和有针对性的人工注释的结合。所处理的每个问题都测试了方法的不同方面，例如处理复杂的衍生和纳入世界知识，这些问题产生了具体的评估框架，以了解所提议的技术的有效性。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。

Modern natural language processing systems are effective at shallow analysis of unstructured text data, performing tasks such as discovering events, identifying the actors of those events, and grouping events with the same actors. Neural networks help make these systems robust to effects like paraphrasing, but still capture mostly superficial text patterns. To answer deeper questions about things like causal relationships between the events in a text, a system might need to combine several pieces of information, abstract away irrelevant details, and incorporate prior world knowledge to arrive at an answer. This project aims to develop systems that can address these challenges: these systems explicitly model reasoning over text and draw on the power of neural networks to do this reasoning in a nuanced way. Such reasoning is explicitly taught to the systems via "handholding" supervision, which encourages the systems to mimic how humans solve a problem and helps them generalize better to new problem instances. This alignment with what humans do also serves to expose the systems' decision-making processes; it provides a form of explanation of their behavior so that one may evaluate them against desired criteria such as equitability.This proposal's technical innovation is focused on two fronts: designing latent variable models and exploiting new types of handholding supervision during model training. These techniques are explored in the context of three challenging problems requiring complex reasoning: (1) solving mathematical word problems; (2) resolving coreference using world knowledge; (3) answering questions from documents. For each problem, new models are proposed centering around discrete derivations of answers, which draw on state-of-the-art tools like attention-based recurrent neural networks to capture the larger context of the reasoning process. The discreteness of the models' decisions provides an anchor to incorporate auxiliary supervision, which is hard to do in fully end-to-end neural models. The nature of the handholding supervision depends on the task and is a combination of incidental supervision, heuristically identified derivations, and targeted human annotation. Each of the addressed problems tests different aspects of the approach, such as handling complex derivations and incorporating world knowledge, and these problems yield concrete evaluation frameworks to understand the efficacy of the proposed techniques.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.