There is a significant disconnect between linguistic theory and modern NLP
practice, which relies heavily on inscrutable black-box architectures.
DisCoCirc is a newly proposed model for meaning that aims to bridge this
divide, by providing neuro-symbolic models that incorporate linguistic
structure. DisCoCirc represents natural language text as a `circuit' that
captures the core semantic information of the text. These circuits can then be
interpreted as modular machine learning models. Additionally, DisCoCirc fulfils
another major aim of providing an NLP model that can be implemented on
near-term quantum computers.
In this paper we describe a software pipeline that converts English text to
its DisCoCirc representation. The pipeline achieves coverage over a large
fragment of the English language. It relies on Combinatory Categorial Grammar
(CCG) parses of the input text as well as coreference resolution information.
This semantic and syntactic information is used in several steps to convert the
text into a simply-typed $\lambda$-calculus term, and then into a circuit
diagram. This pipeline will enable the application of the DisCoCirc framework
to NLP tasks, using both classical and quantum approaches.

提出了 DisCoCirc 模型，用于将自然语言文本表示为电路，以提供可以在经典和量子方法中应用于自然语言处理任务的神经符号模型。此外，还描述了将英语文本转换为 DisCoCirc 表示的软件管道，该管道使用组合的范畴语法解析及核心指代解析信息来转换文本。