Libri UniversitariAmbiguity – Error and bias
Libro adottato a L'Orientale, a.a. 2026/2027 · 1 canale
«Ambiguity – Error and bias» è adottato per Dati Linguistici per le Tecnologie della Traduzione dal prof. Maria Pia Di Buono (Traduzione Specialistica – sede di Napoli – L'Orientale).
Ambiguity – Error and biasCerca su Amazon ›
Come lo indica il docente: Ambiguity, variation, uncertainty, error and bias. In Proceedings of the 14th Linguistic Annotation Workshop
Chi lo adotta
- Dati Linguistici per le Tecnologie della Traduzione – Prof. Maria Pia Di Buono (canale unico)Traduzione Specialistica – sede di Napoli · Laurea magistrale (LM-94) · 1º anno · 2º semestre · Consulenti linguistici · 6 CFU
Programma e testi di ogni canale
Dati Linguistici per le Tecnologie della Traduzione – Prof. Maria Pia Di Buono Canale unico
Corso di laurea: Traduzione Specialistica – sede di Napoli · Laurea magistrale (LM-94) · 1º anno · 2º semestre · Consulenti linguistici · 6 CFU
Il docente indica 1 testo
Ambiguity – Error and biasquesto libroCerca su Amazon ›Da studiare: Pp. 60–69. Blasi, D., Anastasopoulos, A., & Neubig, G. (2022). Systematic inequalities in language technology performance across the world's languages. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1), pp. 5486–5493. Branco, A., Eskevich, M., Frontini, F., Hajič, J., Hinrichs, E., Jong, F. D., ... & Zinn, C. (2023). The CLARIN infrastructure as an interoperable language technology platform for SSH and beyond. Language Resources and Evaluation, pp. 4359–4385. Briva-Iglesias, V., Camargo, J. L. C., & Dogru, G. (2024). Large language models "ad referendum": How good are they at machine translation in the legal domain?, pp. 1–22. Costa-Jussà, M. R., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., ... & NLLB Team. (2022). No language left behind: Scaling human-centered machine translation. (selected material), pp. 4–46, pp. 128–131. Cui, X., Huang, Z., & Adel, N. (2025). Bias in, Bias out: Annotation bias in multilingual large language models. In Proceedings of the Interdisciplinary Workshop on Observations of Misunderstood, Misguided and Malicious Use of Language Models, pp. 1–8. Liu, Y., Cao, J., Liu, C., Ding, K., & Jin, L. (2025). Datasets for large language models: A comprehensive survey. Artificial Intelligence Review, 58(12), 403, pp. 1–62. Lu, H., Yang, H., Huang, H., Zhang, D., Lam, W., & Wei, F. (2024, November). Chain-of-dictionary prompting elicits translation in large language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 958–967. Maurer, M., Linde, M., & Lapesa, G. (2026). Who and What? Using Linguistic Features and Annotator Characteristics to Analyze Annotation Variation, pp. 1–9. Moslem, Y., Haque, R., Kelleher, J., & Way, A. (2022). Domain-specific text generation for machine translation. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 1), pp. 14–23. Moslem, Y., Romani, G., Molaei, M., Kelleher, J., Haque, R., & Way, A. (2023). Domain terminology integration into machine translation: Leveraging large language models. In Proceedings of the Eighth Conference on Machine Translation, pp. 902–907. Noll, R., Frischen, L. S., Boeker, M., Storf, H., & Schaaf, J. (2023). Machine translation of standardised medical terminology using natural language processing: A scoping review. New Biotechnology, 77, pp. 120–128. Opitz, J., Wein, S., & Schneider, N. (2025). Natural language processing relies on linguistics. Computational Linguistics, 51(3), pp. 1009–1020. Palmer, M., & Xue, N. (2010). Linguistic annotation. In The Handbook of Computational Linguistics and Natural Language Processing, pp. 238–269. Pang, J., Ye, F., Wong, D. F., Yu, D., Shi, S., Tu, Z., & Wang, L. (2025). Salute the classic: Revisiting challenges of machine translation in the age of large language models. Transactions of the Association for Computational Linguistics, 13, pp. 73–87. Poncelas, A., Shterionov, D., Way, A., de Buy Wenniger, G. M., & Passban, P. (2018). Investigating backtranslation in neural machine translation. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, pp. 249–256. Röttger, P., Vidgen, B., Hovy, D., & Pierrehumbert, J. (2022). Two contrasting data annotation paradigms for subjective NLP tasks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1–5. Singh, S., Romanou, A., Fourrier, C., Adelani, D. I., Ngui, J. G., Vila-Suero, D., ... & Hooker, S. (2025). Global MMLU: Understanding and addressing cultural and linguistic biases in multilingual evaluation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 18761–18769, pp. 18777–18785. Wang, W., Watanabe, T., Hughes, M., Nakagawa, T., & Chelba, C. (2018). Denoising neural machine translation training with trusted data and online data selection. In Proceedings of the Third Conference on Machine Translation: Research Papers, pp. 133–142. Wassie, A. K., Molaei, M., & Moslem, Y. (2024). Domain-specific translation with open-source large language models: Resource-oriented analysis, pp. 1–9. Zeng, J., Meng, F., Yin, Y., & Zhou, J. (2024). Teaching large language models to translate with comparison. In Proceedings of the AAAI Conference on Artificial Intelligence (38(17)), pp. 19488–19494. Tutti i testi sono disponibili on-lineVerificato sulla scheda ufficiale il 04/10/2026
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