EAGER: Incremental Semantic Sentence Processing Models

从一个复杂句子的许多可能的解释中提取一个单一的含义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范围。 目前人类句子处理的计算模型可以使用单词和句法模式的概率估计来模拟人类阅读行为，但是还不够复杂，无法估计跨多个句子表达的复杂潜在想法的概率。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，以模拟复杂的跨领域意义，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定量关系。建议的扩展是基于一个图形表示的话语结构，它可以逐步构建的时间顺序为句子的处理。与这些图的各个元素相关联的概率然后被组合以获得对输入句子的可能含义的概率估计，然后可以基于这些概率来比较。计算句子处理模型将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进行评估，这些复杂关系如何从自然语言中解码的准确模型可以进一步了解大脑如何工作，并且有朝一日可以让非程序员领域专家向机器解释所需的产品，目标和限制。但目前广泛覆盖的句子处理模型主要集中在建模语法，特别是使用概率上下文无关语法（PCFG）语法。尽管他们的句法复杂，PCFG模型不切实际的假设，词序列生成没有任何连续性的指称意义或任何偏好之间可能的共指和量词范围排序。拟议的工作将开发一个更人性化的语义处理模型，通过增强现有的增量解析器与图形依赖为基础的适应话语表示结构。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，即使是那些跨句子边界。然后，该模型将利用这些依赖关系表示的图形性质，通过估计每个分析的概率作为其组件依赖关系的概率的乘积，基于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。

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.