Situation Entity (SE) segmentation identifies clause-like discourse units focusing on verb constellations. While SE segmentation has been applied to contemporary English as a subtask of SE annotation, systematic guidelines for syntactically ambiguous constructions remain underspecified. We present principled SE segmentation guidelines for contemporary and historical varieties of English and German. Our inter-annotator agreement studies on Late Modern English (1700–1900) and New High German (1650–1900) corpora demonstrate substantial agreement. Using the existing SitEnt corpus in contemporary English, we implement a new automatic segmenter based on XLM-RoBERTa. Our evaluation examines cross-variety and cross-lingual generalization, demonstrating challenges both for human annotation efforts and in transferring segmenters trained on contemporary English to historical varieties. Our code and data are publicly available at https://github.com/coling-unia/sitent-segmenter-law2026.

Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-resource languages generally perform well on SID. However, they struggle with dialectal data, where no standardized form exists and training data is scarce and costly to produce. We explore zero-shot transfer learning for SID, focusing on multiple Bavarian dialects, for which we release a new dataset for the Munich dialect. We evaluate models trained on auxiliary tasks in Bavarian, and compare joint multi-task learning with intermediate-task training. We also compare three types of auxiliary tasks: token-level syntactic tasks, named entity recognition (NER), and language modelling. We find that the included auxiliary tasks have a more positive effect on slot filling than intent classification (with NER having the most positive effect), and that intermediate-task training yields more consistent performance gains. Our best-performing approach improves intent classification performance on Bavarian dialects by 5.1 and slot filling F1 by 8.4 percentage points.