Speaker
Description
Slovenian dialects — among the most diverse in the Slavic world — remain almost invisible to language technology: no dialect corpus with morphosyntactic annotation exists, and standard NLP tools fail on dialect input. I present work toward the first Universal Dependencies (UD) annotated corpus of Slovenian dialects, built with a modular, LLM-assisted pipeline.
The pipeline has four stages. (1) Collection: merge and quality-filter heterogeneous sources (e.g., GOKO, Fran dialect dictionaries, and others smaller sources) and mine dialect–standard glossaries from inline glosses, dictionaries, and word-aligned transcriptions. (2) Normalization: dialect forms are mapped to standard Slovenian through a cascade of deterministic phonological rules, lexicon lookups expanded via Sloleks inflectional paradigms, and — only for unresolved tokens — the Slovenian generative model GaMS as a constrained word-level oracle, under a strict one-to-one token mapping that preserves alignment with the original forms. (3) Annotation: normalized text is parsed with CLASSLA-Stanza; dialect forms are retained and each token receives a variety tag (standard/non-standard/dialectal). (4) Gold data and fine-tuning: a manually corrected subcorpus feeds fine-tuning of SloBERTa and GaMS, closing the loop between rule-based and neural components.
I report first results on normalization accuracy and parser performance, discuss possible synthetic dialect data generation as an augmentation strategy, and argue that hybrid rule–LLM cascades are a realistic path to treebanks for non-standard varieties of low-resource languages.