Euphemisms present a persistent challenge for NLP because their interpretation depends on pragmatic inference, social norms, and contextual cues rather than surface meaning alone. Although Potentially Euphemistic Terms (PET)-based resources have been developed for several languages, Japanese euphemisms remain computationally unexplored despite their close interaction with honorifics, register variation, and orthographic choice. We introduce JP-PET, the first PET-based dataset for Japanese euphemism classification, comprising 1,672 annotated sentences across 101 PETs and ten semantic domains with register metadata. We evaluate two Japanese monolingual transformer models (Rinna RoBERTa and Tohoku BERT) and the multilingual XLM-R under three controlled PET-level data splits that isolate lexical familiarity and generalization to unseen euphemisms. While models achieve strong performance when PETs are shared between training and test data, performance drops substantially under PET-disjoint conditions, indicating reliance on lexical familiarity. Error analysis reveals systematic challenges in politically conventionalized expressions, metaphor-based euphemisms, and orthographic mitigation strategies. JP-PET provides the first benchmark for studying pragmatic meaning in Japanese NLP.

Euphemism detection is often motivated as a pragmatic task: deciding whether a potentially euphemistic term (PET) is used euphemistically depends on context, communicative goals, politeness, topic sensitivity, and sentiment. A natural prediction is that auxiliary supervision from pragmatically related tasks should improve euphemism detection when target supervision is limited. We test this prediction for PET-style classification using XLM-R and mBERT in a feature-based transfer setting: auxiliary-tuned encoders are frozen and probed for euphemism detection. We compare sentence-level pragmatic proxy tasks with expression-level nonliteral tasks and with target inputs that either mark or omit the PET span. The results do not support a simple pragmatic-transfer account. Auxiliary transfer is unreliable, often reducing XLM-R performance and helping mBERT only inconsistently. The largest observed gains come from direct euphemism fine-tuning when the target PET is explicitly marked. The practical implication is to preserve target-span information and report source–target compatibility in transfer experiments.

This study aims to improve the reliability of a new AI collaborative scoring system used to assess the quality of students’ written arguments. The system draws on the Rational Force Model and focuses on classifying the functional relation of each proposition in terms of support, opposition, acceptability, and relevance.