Diplomatic discourse is characterised by a rigorous pragmatic granularity, in which subtle lexical variations encode consequential shifts in legal authority, geopolitical alignment, and institutional commitment. These high-stakes nuances are frequently obscured by the semantic smoothing inherent in general-purpose Large Language Models (LLMs), which prioritize broad linguistic patterns over specialized institutional registers (McGillivray et al, 2020) (Bender et al., 2020).

This paper introduces DiploBERT, a domain-specific transformer encoder developed through Domain-Adaptive Pre-Training (DAPT) on a curated 5.5GB corpus of diplomatic and institutional archives (Gururangan, S., et al., 2020). By modeling the formulaic hedging, strategic ambiguity, and modal precision characteristic of diplomatic prose, DiploBERT transforms patterns that general models perceive as statistical noise into analytically meaningful signals.

This work provides an infrastructural resource for the Digital Humanities, bridging computational language modeling and diplomatic hermeneutics, enabling scholars to move beyond reductive keyword searches toward a context-aware distant reading. In doing so, it offers a new lens for analysing the datafication and transformation of international communications.