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

人类是如何学习、理解和产生语言的？什么是构成听起来自然的句子所必需的心理表征？在一个语言日益多样化的国家，从认知语言学的角度解决这些基本问题，是提高第二语言学习水平、开发更好的自然语言计算机界面的关键。该项目采用了大数据方法，通过记录对话和大型文本数据库来研究说话者和作者之间的适应情况，以推断心理表征。它遵循的基本观点是，适应表明这种心理结构的存在。通过这种方法，研究人员将使用大型文本数据集作为进入人类思维的锁眼。他们将创造一种无偏见的、很大程度上是自动的方法来评估描述大脑如何实现快速、流畅、近乎完美的语言生成的计算模型。该项目的目标是开发一种心理语言学的计算模型，精确地阐明语言产生所需的步骤和表征。由于这些模型是在大规模语言数据上进行测试的，因此可以进行比较和逐步改进。本项目将开发一个认知模型来描述语料库中自然对话中语言产生的一致性。作为结构层面一致性的基础，它将解释和预测句法“启动”效应：例如，“语言学家把实验室钥匙给了他的学生”会启动听者用“学生向编辑展示了他的结果”（目标）来模仿句子结构，而不是“……”给编辑看他的结果”。该模型将用认知心理学研究的一般认知操作（如基于线索的记忆检索）模拟语言产生。它将解释启动的关键特征，包括快速衰减、长期持续和收敛、词汇增强效应和对中间句子的干扰敏感性。该模型将基于认知框架ACT-R，从而将语言处理与记忆的一般定量和计算帐户相结合。广泛的、词汇化的语法形式被用来解释现实生活中的语言数据。

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.