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

现代自然语言处理系统在非结构化文本数据的浅层分析、执行诸如发现事件、识别这些事件的参与者以及将事件与相同参与者分组等任务方面是有效的。神经网络有助于使这些系统对释义等效果具有健壮性，但仍能捕捉到大多数肤浅的文本模式。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，一个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应对这些挑战的系统：这些系统明确地对文本进行推理建模，并利用神经网络的能力以一种微妙的方式进行这种推理。这种推理是通过“握手”监督明确地传授给系统的，这鼓励系统模仿人类如何解决问题，并帮助它们更好地概括新的问题实例。这种与人类所做的一致也有助于暴露系统的决策过程；它提供了一种对系统行为的解释形式，以便人们可以根据公平性等期望的标准来评估它们。本提案的技术创新集中在两个方面：设计潜变量模型和在模型培训期间利用新型的握手监督。这些技巧是在三个需要复杂推理的挑战性问题的背景下探索的：(1)解决数学应用题；(2)使用世界知识解决共指；(3)回答文献中的问题。对于每个问题，新的模型都是围绕答案的离散派生提出的，这些模型利用基于注意力的递归神经网络等最先进的工具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，这在完全端到端的神经模型中是很难做到的。握手监督的性质取决于任务，是附带监督、启发式识别的派生和有针对性的人类注释的组合。每个解决的问题都测试了方法的不同方面，如处理复杂的派生和纳入世界知识，这些问题产生了具体的评估框架，以理解拟议技术的有效性。该奖项反映了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。

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.