Counting trees: A treebank-driven exploration of syntactic variation in speech and writing across languages

TL;DR

This study introduces a new, scalable method for analyzing syntactic differences between speech and writing across languages using dependency treebanks, revealing modality-specific syntactic preferences and limited overlap in structures.

Contribution

It presents a fully inductive, treebank-driven approach to compare syntactic structures in speech and writing across languages, highlighting modality-specific syntactic patterns.

Findings

Spoken corpora have fewer and less diverse syntactic structures than written ones.

Limited overlap exists between spoken and written syntactic inventories.

Speech-specific structures are linked to interactivity and economy of expression.

Abstract

This paper presents a novel treebank-driven approach to comparing syntactic structures in speech and writing using dependency-parsed corpora. Adopting a fully inductive, bottom-up method, we define syntactic structures as delexicalized dependency (sub)trees and extract them from spoken and written Universal Dependencies (UD) treebanks in two syntactically distinct languages, English and Slovenian. For each corpus, we analyze the size, diversity, and distribution of syntactic inventories, their overlap across modalities, and the structures most characteristic of speech. Results show that, across both languages, spoken corpora contain fewer and less diverse syntactic structures than their written counterparts, with consistent cross-linguistic preferences for certain structural types across modalities. Strikingly, the overlap between spoken and written syntactic inventories is very limited: most structures attested in speech do not occur in writing, pointing to modality-specific preferences in syntactic organization that reflect the distinct demands of real-time interaction and elaborated writing. This contrast is further supported by a keyness analysis of the most frequent speech-specific structures, which highlights patterns associated with interactivity, context-grounding, and economy of expression. We argue that this scalable, language-independent framework offers a useful general method for systematically studying syntactic variation across corpora, laying the groundwork for more comprehensive data-driven theories of grammar in use.