EAGER: Grounding Natural Language Inference in Cognitive Processes

能够检测文本相似性对于许多应用都很重要，包括机器翻译、抄袭检测、文本生成、事实核查等。在单词级别，当一个词可以与另一个词互换而几乎没有结果时，两个词往往表示相同的事情。但是，这种方法如何扩展到句子层面和更远的层面呢？根据一种以推理为中心的观点，一个句子的很大一部分意义可以通过每句话的推理光环来理解。“推理光环”是一个句子所具有的所有推论，即隐含的意义。然后，通过比较每个句子或文本所暗示的所有推论来实现两个句子或文本的语义相似性的比较。然而，目前用于检测句子和文本相似性的自然语言处理方法仅限于基于词或子串相似性的度量，这不能充分地捕捉文本的含义。该项目解决了以前方法的局限性，a)丰富了推理的概念和表示，b)学习人们如何自然地对文本的语义关系进行推理。我们方法的新颖性借鉴了认知心理学的既定工作。为了丰富推理的概念，我们引入了a)快速和自动推理(称为类型1)和b)速度更慢、更深思熟虑的推理(称为类型2)之间的区别。例如，当我们在计算钞票小费时识别面孔和类型2时，类型1推理适用。为了了解人们是如何自然推理的，我们使用认知心理学中建立的用于快速和缓慢推理的数据收集协议来收集数据。在数据收集方面，我们实验了一种新的粒度级别，以更好地捕获人类做出的推理范围，并训练新的检测语义推理的计算模型。这项拟议的研究将产生结果、指导方针和新的计算模型，这将导致a)一种研究语言处理中非正式推理的新方法，以及b)改进文本相似性的度量标准。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。

Being able to detect textual similarity is important for many applications including machine translation, detection of plagiarism, text generation, fact checking etc. At the word level, two words tend to mean the same thing when one can be swapped with the other with little or no consequence. But how does this approach extend to the sentence level and beyond? According to an inference-centered view, a significant part of a sentence’s meaning can be understood in terms of the “inferential halo” of each sentence. The “inferential halo” is all the inferences, i.e., implied meanings, that a sentence has. Comparing the semantic similarity of two sentences or text would then be accomplished by comparing all the inferences that each sentence or text implies. However, current Natural Language Processing approaches to detecting sentence and text similarities are limited to measures based on word or substring similarities which do not capture adequately the meaning of a text. This project addresses the limitations of previous approaches a) enriching the notion and representation of inference and b) learning how people naturally reason about semantic relations of texts. The novelty of our approach draws on established work in Cognitive Psychology. For the enrichment of the notion of inference, we introduce the distinction between a) quick and automatic reasoning, known as Type 1, and b) slower and more deliberate reasoning, known as Type 2. Type 1 reasoning applies when for example we recognize a face and Type 2 when we calculate the tip for a bill. To learn how people naturally reason, we collect data using data collection protocols established in Cognitive Psychology for quick and slow reasoning. For data collection, we experiment with a novel level of granularity to better capture the range of inferences made by humans and train new computational models of detecting semantic inferences. The proposed research will yield results, guidelines, and new computational models that will lead to a) a novel way of studying informal reasoning in language processing and b) improved metrics of textual similarity.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.