EAGER: Incremental Semantic Sentence Processing Models

EAGER：增量语义句子处理模型

• 批准号: 1551313
• 负责人: William Schuler
• 金额: $ 11.66万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551313&HistoricalAwards=false
• 关键词: EAGER; Incremental; Semantic; Sentence; Processing

Extracting a single meaning from the many possible interpretations of a complex sentence is one of the most sophisticated of human abilities, and is still beyond the reach of most artificial language processing systems. Current computational models of human sentence processing can simulate human reading behavior using probability estimates of words and syntactic patterns, but are not yet sophisticated enough to estimate the probability of complex underlying ideas that are expressed across multiple sentences. This exploratory EAGER project extends human sentence processing models beyond these word- and syntax-based techniques to model complex cross-sentential meaning involving coreference relationships between pronouns and their antecedents, and quantificational relationships between individuals and groups. The proposed extensions are based on a graphical representation of discourse structure, which can be constructed incrementally in time order as sentences are processed. Probabilities associated with individual elements of these graphs are then combined to obtain probability estimates over possible meanings of input sentences, which can then be compared based on these probabilities. The resulting computational sentence processing models are then evaluated on explanatory text from on-line encyclopedia articles and on existing broad-coverage psycholinguistic datasets.Accurate models of how these complex relationships are decoded from natural language could further our understanding of how the brain works, and may someday allow non-programmer domain experts to explain desired products, goals and constraints to machines. But current broad-coverage sentence processing models are focused primarily on modeling syntax, in particular using probabilistic context-free grammar (PCFG) surprisal. Despite their syntactic sophistication, PCFG models make unrealistic assumptions that word sequences are generated without any continuity of referential meaning or any preferences among possible coreference and quantifier scope orderings. The proposed work will develop a more human-like semantic processing model by augmenting an existing incremental parser with a graphical dependency-based adaptation of discourse representation structures. The proposed semantic processing model will define complete semantic dependency representations of sentences, including quantifier scope and coreference relationships, even those that cross sentence boundaries. The model will then exploit the graphical nature of these dependency representations by estimating the probability of each analysis as the product of the probabilities of its component dependencies, based on the distributional similarity of each dependency's source predicate to the other predicates connected to its destination.

从复杂句子的许多可能的解释中提取单一含义是人类能力中最复杂的含义之一，并且仍然超出了大多数人造语言处理系统的影响力。 人类句子处理的当前计算模型可以使用单词和句法模式的概率估计来模拟人的阅读行为，但尚未足够复杂，无法估算出在多个句子中表达的复杂基本思想的概率。这个探索性渴望的项目将人类句子处理模型扩展到这些基于单词和语法的技术之外，以模拟复杂的跨索引含义，涉及代词及其先例之间的核心关系，以及个人与群体之间的量化关系。所提出的扩展基于话语结构的图形表示，可以随着句子的处理，可以按时间顺序逐步构造。然后将与这些图的各个元素相关的概率合并，以获得对输入句子可能含义的概率估计，然后可以根据这些概率进行比较。然后，通过在线百科全书文章和现有的宽覆盖心理语言数据集对所产生的计算句子处理模型进行评估。准确地介绍了这些复杂关系如何从自然语言中解码如何进一步解码我们对大脑的理解，并且有一天可能会允许非编织者域来解释所需的产品的专家来解释所需的产品产品和限制性的产品和约束。但是当前的宽覆盖句子处理模型主要集中在建模语法上，特别是使用概率无上下文语法（PCFG）惊奇。尽管PCFG模型具有句法复杂性，但仍为单词序列生成而没有任何参考含义的连续性或可能的核心和量词范围顺序中的任何偏好。拟议的工作将通过通过基于图形依赖关系的话语表示结构来增强现有的增量解析器来开发更类似人类的语义处理模型。提出的语义处理模型将定义句子的完整语义依赖性表示，包括量化范围和核心关系，甚至是跨句子边界的关系。然后，基于每个依赖关系源的分布相似性，将每个分析的概率估计为其组件依赖性的概率的乘积来利用这些依赖关系表示的图形性质，这是基于每个依赖源的分布相似性，源于连接到其目的地的其他谓词。