This paper introduces EmplifAI, a Japanese empathetic dialogue dataset designed to support patients coping with chronic medical conditions. They often experience a wide range of positive and negative emotions (e.g., hope and despair) that shift across different stages of disease management. EmplifAI addresses this complexity by providing situation-based dialogues grounded in 28 fine-grained emotion categories, adapted and validated from the GoEmotions taxonomy. The dataset includes 280 medically contextualized situations and 4,125 two-turn dialogues, collected through crowdsourcing and expert review.To evaluate emotional alignment in empathetic dialogues, we assessed model predictions on situation–dialogue pairs using BERTScore across multiple large language models (LLMs), achieving F1 scores of ≤ 0.83. Fine-tuning a baseline Japanese LLM (LLM-jp-3.1-13b-instruct4) with EmplifAI resulted in notable improvements in fluency, general empathy, and emotion-specific empathy. Furthermore, we compared the scores assigned by LLM-as-a-Judge and human raters on dialogues generated by multiple LLMs to validate our evaluation pipeline and discuss the insights and potential risks derived from the correlation analysis.

The distribution of most dialectal variants have not only spatial but also temporal patterns. Based on the ‘apparent time hypothesis’, much of dialect change is happening through younger speakers accepting innovations. Thus, synchronic diversity can be interpreted diachronically. With the assumption of the ‘contact effect’, i.e. contact possibility (contact and isolation) between speaker communities being responsible for language change, and the apparent time hypothesis, we aim to predict the usage of dialectal variants. In this paper we model the contact possibility based on two of the most important factors in sociolinguistics to be affecting language change: age and distance. The first steps of the approach involve modeling contact possibility using a logistic predictor, taking the age of respondents into account. We test the global, and the local role of age for variation where the local level means spatial subsets around each survey site, chosen based on k nearest neighbors. The prediction approach is tested on Swiss German syntactic survey data, featuring multiple respondents from different age cohorts at survey sites. The results show the relative success of the logistic prediction approach and the limitations of the method, therefore further proposals are made to develop the methodology.