8 papers · 1 filter
Lexically conditioned realization ambiguity in Korean predicate morphology
Wonjun Oh, KyungTae Lim, Jungyeul Park
This paper examines Korean surface realization as distinct from morphological analysis. It asks whether a sequence of canonical morphemes and grammatical category labels uniquely d…
Representing and Parsing Korean Constituency Structure at Different Levels of Granularity
Jungyeul Park, KyungTae Lim, Zihao Huang +3
Korean constituency parsing raises a representational challenge because the terminal units of a phrase-structure tree do not straightforwardly correspond to simple surface words. K…
TELLME: Test-Enhanced Learning for Language Model Enrichment
Minjun Kim, Inho Won, Hyeonseok Lim +6
Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large language models. However, CPT has consistently been accompanied by challenges, such…
Annotating Korean adnominal ending constructions in corpus data: Beyond relative-clause identification
Jungyeul Park, Chulwoo Park
The Korean adnominal ending \texttt{ETM} occurs in diverse noun-modifying constructions, including relative-clause-like modifiers, adjectival and copular forms, bound-noun construc…
Better heads do not guarantee better binarized constituency parsing
Zeyao Qi, Yige Chen, Eitan Klinger +2
We revisit punctuation-aware tree binarization for constituency parsing and ask whether dependency-induced headedness improves binary parser supervision. Although learned heads sub…
Chinese Word Boundary Recovery through Character Alignment Projection
Lusha Wang, Yuchen Li, Su Yuan +1
Chinese word segmentation is especially fragile in non-standard text, where language learner errors and other character-level divergences disrupt the word boundaries assumed by dow…
Learning Constituent Headedness
Zeyao Qi, Yige Chen, KyungTae Lim +2
Headedness is widely used as an organizing device in syntactic analysis, yet constituency treebanks rarely encode it explicitly and most processing pipelines recover it procedurall…
TREX: Tokenizer Regression for Optimal Data Mixture
Inho Won, Hangyeol Yoo, Minkyung Cho +3
Building effective tokenizers for multilingual Large Language Models (LLMs) requires careful control over language-specific data mixtures. While a tokenizer's compression performan…