CompCog: Modeling syntactic priming in language production according to corpus data

人类如何学习、理解和创造语言？什么是组成听起来自然的句子所必需的心理表征？从认知语言学的角度解决这些基本问题，对于在一个语言日益多样化的国家改善第二语言学习，以及开发更好的自然语言计算机界面至关重要。这个项目遵循大数据的方法，它着眼于说话者和作者在录音对话和大型文本数据库中的适应，以便推断心理表征。它遵循的基本思想是，适应表明存在这样的心理结构。在这种方法下，研究人员将使用大型文本数据集作为进入人类思维的钥匙孔。他们将创建一种公正的、基本上自动的方式来评估计算模型，这些模型描述了大脑如何实现快速、流畅、近乎完美的语言产生。该项目的目标是开发一种心理语言学的计算模型，准确地阐明语言产生所需的步骤和表征。这些模型可以进行增量比较和改进，因为它们是在大规模语言数据上进行测试的。这个项目将开发一个认知模型来描述语言产生中的对齐，就像在语音语料库的自然对话中发现的那样。作为结构层面对齐的基础，它将解释和预测句法上的“启动”效应：例如，“语言学家把实验室钥匙给了他的学生”，用“学生向编者展示了他的结果”而不是“……向编者展示了他的结果”来引导听者反映句子结构。该模型将用认知心理学研究的一般认知操作来模拟语言产生，例如基于线索的记忆提取。它将解释启动的关键特征，包括快速衰退、长期持续和收敛、词汇促进效应以及对插入句子的干扰敏感性。该模型将基于认知框架ACT-R，从而将语言处理与对记忆的一般量化和计算帐户相结合。广泛的、词汇化的句法形式主义被用来解释真实的语言数据。

How do humans learn, understand, and produce language? What are the mental representations necessary to compose natural-sounding sentences? Addressing such fundamental questions from cognitive linguistics is key to improving second-language learning in a nation that is becoming increasingly linguistically diverse, and to develop better natural-language computer interfaces. This project follows a big-data approach in that it looks at adaptation between speakers and authors in recorded conversations and large text databases in order to infer mental representations. It follows the basic idea that adaptation indicates the presence of such mental structures. With this methodology, the researchers will use large text datasets as a keyhole into the human mind. They will create an unbiased and largely automatic way to evaluate computational models that describe how the mind achieves fast, fluent, near-perfect language production.The goal of the project is to develop a psycholinguistic, computational model that spells out precisely the steps and representations necessary for language production. The models can be compared and improved incrementally because they are tested on large-scale language data. This project will develop a cognitive model to describe alignment in language production as found in natural dialogue in speech corpora. As a basis for alignment at the structural level, it will explain and predict syntactic "priming" effects: E.g., "The linguist gave the lab keys to his student" primes a listener to mirror the sentence structure with "The student showed his results to the editor" (target), rather than "... showed the editor his results". The model will simulate language production with general cognitive operations studied by cognitive psychology, such as cue-based memory retrieval. It will account for key characteristics of priming, including rapid decay, long-term persistence and convergence, lexical boost effects, and interference sensitivity to intervening sentences. The model will be based on a cognitive framework, ACT-R, thereby integrating language processing with general quantitative and computational accounts of memory. A broad-coverage, lexicalized syntax formalism is used to account for real-life language data.