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。

CCG 主题电路的流程管道

A Pipeline For Discourse Circuits From CCG

This paper concerns the structure of meanings within natural language.
Earlier, a framework named DisCoCirc was sketched that (1) is compositional and
distributional (a.k.a. vectorial); (2) applies to general text; (3) captures
linguistic `connections' between meanings (cf. grammar) (4) updates word
meanings as text progresses; (5) structures sentence types; (6) accommodates
ambiguity. Here, we realise DisCoCirc for a substantial fragment of English.
When passing to DisCoCirc's text circuits, some `grammatical bureaucracy' is
eliminated, that is, DisCoCirc displays a significant degree of (7) inter- and
intra-language independence. That is, e.g., independence from word-order
conventions that differ across languages, and independence from choices like
many short sentences vs. few long sentences. This inter-language independence
means our text circuits should carry over to other languages, unlike the
language-specific typings of categorial grammars. Hence, text circuits are a
lean structure for the `actual substance of text', that is, the inner-workings
of meanings within text across several layers of expressiveness (cf. words,
sentences, text), and may capture that what is truly universal beneath grammar.
The elimination of grammatical bureaucracy also explains why DisCoCirc: (8)
applies beyond language, e.g. to spatial, visual and other cognitive modes.
While humans could not verbally communicate in terms of text circuits, machines
can.
We first define a `hybrid grammar' for a fragment of English, i.e. a
purpose-built, minimal grammatical formalism needed to obtain text circuits. We
then detail a translation process such that all text generated by this grammar
yields a text circuit. Conversely, for any text circuit obtained by freely
composing the generators, there exists a text (with hybrid grammar) that gives
rise to it. Hence: (9) text circuits are generative for text.

本文利用 DisCoCirc 框架，提出了一种适用于自然语言的生成文本电路，该电路独立于语音和语言，并能够在不同表现层次上捕获和更新单词、句子和文本内涵，并通过构建混合语法和翻译过程，实现了文本生成和解析的循环互逆。

将文本提炼为电路

Distilling Text into Circuits

DisCoCirc is a newly proposed framework for representing the grammar and
semantics of texts using compositional, generative circuits. While it
constitutes a development of the Categorical Distributional Compositional
(DisCoCat) framework, it exposes radically new features. In particular, [14]
suggested that DisCoCirc goes some way toward eliminating grammatical
differences between languages. In this paper we provide a sketch that this is
indeed the case for restricted fragments of English and Urdu. We first develop
DisCoCirc for a fragment of Urdu, as it was done for English in [14]. There is
a simple translation from English grammar to Urdu grammar, and vice versa. We
then show that differences in grammatical structure between English and Urdu -
primarily relating to the ordering of words and phrases - vanish when passing
to DisCoCirc circuits.

本文提出了一种新的框架 DisCoCirc，用于使用组合、生成电路来表示文本的语法和语义，证明了针对英语和乌尔都语的限制性片段，DisCoCirc 在消除语言之间的语法差异方面起到了一定作用。

DisCoCirc 的文本电路的语言无关性：英语和乌尔都语

Language-independence of DisCoCirc's Text Circuits: English and Urdu

In previous work we gave a mathematical foundation, referred to as DisCoCat,
for how words interact in a sentence in order to produce the meaning of that
sentence. To do so, we exploited the perfect structural match of grammar and
categories of meaning spaces. Here, we give a mathematical foundation, referred
to as DisCoCirc, for how sentences interact in texts in order to produce the
meaning of that text. First we revisit DisCoCat. While in DisCoCat all meanings
are fixed as states (i.e. have no input), in DisCoCirc word meanings correspond
to a type, or system, and the states of this system can evolve. Sentences are
gates within a circuit which update the variable meanings of those words. Like
in DisCoCat, word meanings can live in a variety of spaces e.g. propositional,
vectorial, or cognitive. The compositional structure are string diagrams
representing information flows, and an entire text yields a single string
diagram in which word meanings lift to the meaning of an entire text. While the
developments in this paper are independent of a physical embodiment (cf.
classical vs. quantum computing), both the compositional formalism and
suggested meaning model are highly quantum-inspired, and implementation on a
quantum computer would come with a range of benefits. We also praise Jim Lambek
for his role in mathematical linguistics in general, and the development of the
DisCo program more specifically.

本研究提出了一种数学基础 DisCoCirc，用于描述句子在文本中相互作用以产生文本含义的过程，通过组合结构及字符串图表示信息流，进而产生整篇文本的含义。