Intelligent Decision Support for Article Translation in Multilingual Newsrooms
| Tipo de publicação: | Inproceedings |
| Citação: | |
| Booktitle: | 41th ACM/SIGAPP Symposium on Applied Computing |
| Ano: | 2026 |
| Mês: | March |
| Páginas: | 806-814 |
| Publisher: | ACM |
| Location: | Thessaloniki, Greece |
| DOI: | https://doi.org/10.1145/3748522.3779719 |
| Resumo: | We present an intelligent decision support system, powered by AI-driven prediction, designed to assist multilingual newsrooms in selecting articles for cross-regional translation as part of the digital transformation of journalism. Trained on 15,933 German-language articles from a major Swiss publisher, the system combines multilingual BERT embeddings with 41 engineered features and incorporates real-time editorial feedback through an active learning loop. It achieves an accuracy of 85.0%. During deployment, F1 improved from 77.5% to 81.2% after four weeks of feedback-driven exemplar refresh. Ablation studies indicate that sentiment polarity, regional relevance, and person-type named entities are the most influential features. The interface highlights key factors, ensuring transparency and consistency with editorial practice. By pairing hybrid NLP with human-in-the-loop prompting, the approach operationalizes intelligent translation triage in a live newsroom while preserving human control over final decisions. |
| Palavras-chave: | Cross-lingual translation, Decision support systems, Human-in-the-loop AI, Hybrid AI models, Intelligent systems, Media prediction, Multilingual newsrooms |
| Autores | |
| Adicionado por: | [] |
| Total mark: | 0 |
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