Large language models write Hindi that carries the shape of English, and measuring how often is the open problem, because the phraselevel instruments available are lexical and brittle. We build a 26-pair Hindi contrast set over eight idiom families, verified by three native Hindi speakers. Prompt framing moves supplied-candidate scores toward the calque: an English-literal cue shifts them in 8 of 8 families, and a separate translate-from-English instruction shifts them again, with the direction holding on a family split frozen before the outcome was seen. Free generation appears to show far less calquing, so we audit the lexical indicator behind that appearance against native-speaker judgement. Its precision on its own positives is 1.00, while 24 of 60 sampled negatives are calques by majority verdict, and the misses concentrate in the families where its exact-match trigger was never observed to fire. The apparent scarcity is substantially a property of the instrument. We recommend that detectors of this form report coverage alongside hits.

Multilingual emotion detection is a critical challenge in natural language processing, enabling applications in sentiment analysis, mental health monitoring, and user engagement. However, existing models struggle with overlapping emotions, intensity quantification, and cross-lingual adaptation, particularly in low-resource languages. This study addresses these challenges as part of SemEval-2025 Task 11 by leveraging language-specific transformer models for multi-label classification (Track A), intensity prediction (Track B), and cross-lingual generalization (Track C). Our models achieved strong performance in Russian (Track A: 0.848 F1, Track B: 0.8594 F1) due to emotion-rich pretraining, while Chinese (0.483 F1) and Spanish (0.6848 F1) struggled with intensity estimation. Track C faced significant cross-lingual adaptation issues, with Russian (0.3102 F1), Chinese (0.2992 F1), and Indian (0.2613 F1) highlighting challenges in low-resource settings. Despite these limitations, our findings provide valuable insights into multilingual emotion detection. Future work should enhance cross-lingual representations, address data scarcity, and integrate multimodal information for improved generalization and real-world applicability.