To model behavioral and neural correlates of language comprehension in
naturalistic environments, researchers have turned to broad-coverage tools from
natural-language processing and machine learning. Where syntactic structure is
explicitly modeled, prior work has relied predominantly on context-free
grammars (CFG), yet such formalisms are not sufficiently expressive for human
languages. Combinatory Categorial Grammars (CCGs) are sufficiently expressive
directly compositional models of grammar with flexible constituency that
affords incremental interpretation. In this work we evaluate whether a more
expressive CCG provides a better model than a CFG for human neural signals
collected with fMRI while participants listen to an audiobook story. We further
test between variants of CCG that differ in how they handle optional adjuncts.
These evaluations are carried out against a baseline that includes estimates of
next-word predictability from a Transformer neural network language model. Such
a comparison reveals unique contributions of CCG structure-building
predominantly in the left posterior temporal lobe: CCG-derived measures offer a
superior fit to neural signals compared to those derived from a CFG. These
effects are spatially distinct from bilateral superior temporal effects that
are unique to predictability. Neural effects for structure-building are thus
separable from predictability during naturalistic listening, and those effects
are best characterized by a grammar whose expressive power is motivated on
independent linguistic grounds.

通过使用组合范畴语法（CCG）作为语法模型，研究人员发现可以更好地理解自然环境中语言理解的行为和神经相关性。使用 fMRI 收集参与者听故事期间的神经信号，研究发现 CCG 在解释神经信号方面的有效性优于上下文无关文法，并可以从可选附加语方面解释。

使用 CCG 分析和大型语言模型建模大脑中的结构构建

Modeling structure-building in the brain with CCG parsing and large language models

Previous CCG supertaggers usually predict categories using multi-class
classification. Despite their simplicity, internal structures of categories are
usually ignored. The rich semantics inside these structures may help us to
better handle relations among categories and bring more robustness into
existing supertaggers. In this work, we propose to generate categories rather
than classify them: each category is decomposed into a sequence of smaller
atomic tags, and the tagger aims to generate the correct sequence. We show that
with this finer view on categories, annotations of different categories could
be shared and interactions with sentence contexts could be enhanced. The
proposed category generator is able to achieve state-of-the-art tagging (95.5%
accuracy) and parsing (89.8% labeled F1) performances on the standard CCGBank.
Furthermore, its performances on infrequent (even unseen) categories,
out-of-domain texts and low resource language give promising results on
introducing generation models to the general CCG analyses.

通过生成类别而不是分类类别，我们提出了一种新的 CCG supertagger 方法，将每个类别分解为一系列更小的标记，以此更好地处理类别间的关系并增强与句子上下文的交互。该方法能够在标准 CCGBank 上实现最先进的标识 (95.5% 精确度) 和分析 (89.8% 标记 F1)，并在罕见类别、领域外文本和资源匮乏语言方面取得了有希望的结果。