WALS and Grambank encode typological information in different forms: WALS assigns one value to each multi-valued feature, whereas Grambank mostly records the availability of constructions. Computational pipelines commonly expand both into independent binary columns. We compare that binary encoding with a categorical objective that jointly normalizes the values of each feature. The models otherwise share architecture, data, optimization and capacity. On WALS, the categorical objective recovers more of the structure implicit in feature definitions: distances track ordinal scales, compositional dimensions become consistent directions, and 45% of cross-feature nearest neighbours share a grammatical domain, compared with 20% for the binary model. The advantage persists under reparameterization-invariant diagnostics. External evaluations show the same pattern: categorical embeddings rank independently supported implicational universals more strongly, recover the two clause orders associated with languages lacking a single dominant order even when the corresponding feature is withheld, and correlate more strongly with corpus word-order variability among languages assigned the same WALS label. Held-out-branch prediction differs less between objectives, especially on Grambank. Preserving feature-level competition therefore changes representation geometry even when predictive accuracy changes little.

While the study of diachronic semantic change has advanced alongside recent computational developments, structured lexical resources that reflect semantic evolution remain scarce for many languages, including Ancient Chinese. By systematizing the diachronic transformations within the Chinese Text Project (ctext, a large corpus of Ancient Chinese), we aim to bridge the gap between traditional philological inquiry and contemporary computational linguistics. This study proposes a pipeline that extracts contextualized embeddings from GujiBERT-fan, a language model pre-trained on pre-modern Chinese, and applies dynamic hierarchical clustering to identify distinct senses across historical periods. The pipeline operates at two levels: a global clustering that aggregates data across all periods to capture the full semantic space, and local clustering within each dynasty to reveal period-specific usage patterns. We test the pipeline with a pilot study on the character 手 (shǒu, “hand”) across eight dynastic periods, covering over 185,000 occurrences. The results show that the pipeline can capture the diachronic shift from concrete to abstract senses, demonstrating its potential as a scalable method for mapping semantic evolution in historical languages.

In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019. This year, the campaign included five shared tasks, including one task re-run – German Dialect Identification (GDI) – and four new tasks – Cross-lingual Morphological Analysis (CMA), Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT), Moldavian vs. Romanian Cross-dialect Topic identification (MRC), and Cuneiform Language Identification (CLI). A total of 22 teams submitted runs across the five shared tasks. After the end of the competition, we received 14 system description papers, which are published in the VarDial workshop proceedings and referred to in this report.