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.

从一个复杂句子的许多可能的解释中提取一个单一的意思是人类最复杂的能力之一，并且仍然超出了大多数人工语言处理系统的能力。目前人类句子处理的计算模型可以通过对单词和句法模式的概率估计来模拟人类的阅读行为，但还不够复杂，无法估计跨多个句子表达的复杂潜在思想的概率。这个探索性的EAGER项目将人类句子处理模型扩展到这些基于单词和句法的技术之外，以模拟复杂的跨句意义，包括代词及其先行词之间的共指关系，以及个人和群体之间的量化关系。所提出的扩展是基于话语结构的图形表示，可以随着句子的处理按时间顺序逐步构建。然后将与这些图的各个元素相关的概率组合起来，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对这些句子进行比较。然后在在线百科全书文章的解释性文本和现有的广泛覆盖的心理语言学数据集上评估所得的计算句子处理模型。这些复杂关系是如何从自然语言中解码出来的准确模型，可以进一步加深我们对大脑工作方式的理解，也许有一天，非程序员领域的专家可以向机器解释想要的产品、目标和约束。但是目前广泛的句子处理模型主要集中在语法建模上，特别是使用概率上下文无关语法（PCFG）。尽管PCFG模型的语法很复杂，但它做出了不切实际的假设，即生成的词序列没有任何指称意义的连续性，也没有可能的共指和量词范围顺序之间的任何偏好。提出的工作将通过使用基于图形依赖性的话语表示结构的适应来增强现有的增量解析器，从而开发更像人类的语义处理模型。所提出的语义处理模型将定义句子的完整语义依赖表示，包括量词范围和共指关系，甚至那些跨越句子边界的关系。然后，该模型将利用这些依赖关系表示的图形特性，根据每个依赖关系的源谓词与连接到其目的地的其他谓词的分布相似性，将每个分析的概率估计为其组件依赖关系的概率的乘积。