Anàlisi de la detecció d’errors tipogràfics en el text original per part de Google Translate i DeepL Translate en els àmbits econòmic i jurídic

Bibliographic References

• Anastasopoulos, Antonios. (2019). An analysis of source-side grammatical errors in NMT. In Tal Linzen, Grzegorz Chrupała, Yonatan Belinkov, & Dieuwke Hupkes (Eds.), Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (pp. 213-223). Association for Computational Linguistics. 10.18653/v1/W19-4822
• Araghi, Sahar, Palangkaraya, Alfons, & Webster, Elizabeth. (2023). The impact of language translation quality on commerce: The example of patents. Journal of International Business Policy.
• Arpit, Devansh, Jastrzębski, Stanisław, Ballas, Nicolas, Krueger, David, Bengio, Emmanuel, Kanwal, Maxinder S., Maharaj, Tegan, Fischer, Asja, Courville, Aaron, Bengio, Yoshua, & Lacoste-Julien, Simon. (2017). A closer look at memorization in deep networks. In Doina Precup, & Yee Whye Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, PMLR, 70 (pp. 233-
• . Proceedings on Machine Learning Research.
• Belinkov, Yonatan, & Bisk, Yonatan. (2018, April 30-May 3). Synthetic and natural noise both break neural machine translation [Conference presentation]. 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada. https://doi.org/10.48550/arXiv.1711.02173
• Bengio, Samy, Vinyals, Oriol, Jaitly, Navdeep, & Shazeer, Noam. (2015). Scheduled sampling for sequence prediction with recurrent neural networks. In Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, & Roman Garnett (Eds.), NIPS’15: Proceedings of the 28th International Conference on Neural Information Processing Systems – Vol. 1 (pp. 1171-1179).
• Bergmanis, Toms, Stafanovičs, Artūrs, & Pinnis, Mārcis. (2020). Robust neural machine translation: modeling orthographic and interpunctual variation. In Andrius Utka, Jurgita Vaičenonienė, Jolanta Kovalevskaitė, & Danguolė Kalinauskaitė (Eds.), Human Language Technologies – The Baltic Perspective: Proceedings of the Ninth International Conference Baltic HLT (pp. 80-86). IOS Press.
• Briva-Iglesias, Vicent. (2021). Traducción humana vs. traducción automática: análisis contrastivo e implicaciones para la aplicación de la traducción automática en traducción jurídica. Mutatis Mutandis, 14(2), 571-600. https://doi.org/10.17533/udea.mut.v14n2a14
• Conijn, Rianne. (2020). The keys to writing: A writing analytics approach to studying writing processes using keystroke logging [Unpublished doctoral dissertation]. Tilburg University and University of Antwerp.
• Conijn, Rianne, van Zaanen, Menno, Leijten, Mariëlle, & Van Waes, Luuk. (2019). How to typo? Building a process-based model of typographic error revisions. The Journal of Writing Analytics, 3, 69-95. https://doi.org/10.37514/JWA-J.2019.3.1.05
• Costa, Ângela, Ling, Wang, Luís, Tiago, Correia, Rui, & Coheur, Luísa. (2015). A linguistically motivated taxonomy for machine translation error analysis. Machine Translation, 29(2), 127-161.
• Chakravarthi, Bharathi Raja, Rani, Priya, Arcan, Mihael, & McCrae, John P. (2021). A survey of orthographic information in machine translation. SN Computer Science, 2(4), Article 330. https://doi.org/10.1007/s42979-021-00723-4
• Chen, Tsong Yueh, Cheung, Shing Chi, & Yiu, Siu Ming. (1998). Metamorphic testing: A new approach for generating next test cases (Technical report HKUST-CS98-01). Department of Computer Science, The Hong Kong University of Science and Technology. https://doi.org/10.48550/arXiv.2002.12543
• Cheng, Yong, Tu, Zhaopeng, Meng, Fandong, Zhai, Junjie, & Liu, Yang. (2018). Towards robust neural machine translation. In Iryna Gurevych, & Yusuke Miyao (Eds.), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers) (pp. 1756-1766). Association for Computational Linguistics. http://dx.doi.org/10.18653/v1/P18-1163
• Daems, Joke. (2016). A translation robot for each translator? A comparative study of manual translation and post-editing of machine translations: process, quality and translator attitude [Unpublished doctoral dissertation]. Ghent University.
• De Iriarte, Juan. (1774). Juan de Iriarte, Obras sueltas (Tomo II). Imprenta de Don Francisco Manuel de Mena.
• Elbayad, Maha, Ustaszewski, Michael, Esperança-Rodier, Emmanuelle, Manquat, Francis Brunet, Verbeek, Jakob, & Besacier, Laurent. (2020). Online versus offline NMT quality: An in-depth analysis on English-German and German-English. In Donia Scott, Nuria Bel, & Chengqing Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics (pp. 5047-5058). International Committee on Computational Linguistics. 10.18653/v1/2020.coling-main.443
• González Vallejo, Rubén. (2022). De errores y erratas en el lenguaje jurídico: una reflexión acerca de la traducción automática. In Nuria Fernández-Quesada, & Santiago Rodríguez-Rubio (Eds.), Detección y tratamiento de errores y erratas: Un diagnóstico para el siglo XXI (pp. 185-201). Dykinson.
• Heigold, Georg, Varanasi, Stalin, Neumann, Günter, & van Genabith, Joseph. (2018). How robust are character-based word embeddings in tagging and MT against wrod scramlbing or randdm nouse? In Colin Cherry, & Graham Neubig (Eds.), Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Vol. 1: Research Track) (pp. 68-80). Association for Machine
• Translation in the Americas.
• He, Pinjia, Meister, Clara, & Su, Zhendong. (2020). Structure-invariant testing for machine translation. In Gregg Rothermel, & Doo-Hwan Bae (Eds.), ICSE ‘20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings (pp. 961-973). Association for Computing Machinery.
• Hirschmann, Fabian, Nam, Jinseok, & Fürnkranz, Johannes. (2016). What makes word-level neural machine translation hard: A case study on English-German translation. In Yuji Matsumoto, & Rashmi Prasad (Eds.), Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (pp. 3199-3208). COLING 2016 Organizing Committee.
• Jurafsky, Daniel, & Martin, James H. (2023). Speech and language processing: Spelling correction and the noisy channel. Stanford University.
• Killman, Jeffrey, & Rodríguez-Castro, Mónica. (2022). Post-editing vs. translating in the legal context: Quality and time effects from English to Spanish. Revista de Llengua i Dret, Journal of Language and Law, 78, 56-72. http://dx.doi.org/10.2436/rld.i78.2022.3831
• Larriba Flor, Antonio Manuel. (2017). Character-based neural machine translation [Unpublished master’s dissertation]. Universitat Politècnica de València.
• Lee, Dickson T. S., Zhou, Zhi Quan, & Tse, T. H. (2020). Metamorphic robustness testing of Google Translate (Technical report HKUCS-TR20-03). Department of Computer Science, The Hong Kong University of Science and Technology.
• Lew, Robert, & Mitton, Roger. (2012). Online English learners’ dictionaries and misspellings: One year on. International Journal of Lexicography, 26(2), 219-233. https://doi.org/10.1093/ijl/ecs016
• Li, Yitong, Cohn, Trevor, & Baldwin, Timothy. (2017). Robust training under linguistic adversity. In Mirella Lapata, Phil Blunsom, & Alexander Koller (Eds.), Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Vol. 2, Short Papers (pp. 21-27). Association for Computational Linguistics.
• Li, Zilong, Xiao, Lei, Lin, Ruijin, & Xiao, Zhengxinchao. (2023). Metamorphic robustness testing for DeepL translation. Journal of Physics (Conference Series), 2456, Article 012018.
• Luong, Minh-Thang, Sutskever, Ilya, Le, Quoc. V., Vinyals, Oriol, & Zaremba, Wojciech. (2015). Addressing the rare word problem in neural machine translation. In Chengqing Zong, & Michael Strube (Eds.), Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol. 1: Long Papers) (pp. 11-19). Association for Computational Linguistics.
• Maldonado González, María Concepción, & Liébana González, María. (2023). La traducción automática y su uso en la redacción de textos digitales: Análisis de algunos documentos reales de trabajo. Círculo de Lingüística Aplicada a la Comunicación, 95, 133-161. https://doi.org/10.5209/clac.79337
• Mateo Martínez, José. (2009). Diccionario de términos de la banca. Ariel.
• Munkova, Dasa, Munk, Michal, Welnitzova, K., & Jakabovicova, Johanna. (2021). Product and process analysis of machine translation into the inflectional language. SAGE Open, 11(4), 1-13. https://doi.org/10.1177/21582440211054501
• Parra-Galiano, Silvia. (2022). ¿Correcciones o mejoras textuales? Cuándo intervenir en la revisión y corrección de traducciones. In Nuria Fernández-Quesada, & Santiago Rodríguez-Rubio (Eds.), Detección y tratamiento de errores y erratas: Un diagnóstico para el siglo XXI (pp. 103-121). Dykinson.
• Raunak, Vikas, Menezes, Arul, & Junczys-Dowmunt, Marcin. (2021). The curious case of hallucinations in neural machine translation. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, & Yichao Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association
• for Computational Linguistics: Human Language Technologies (pp. 1172-1183). Association for Computational Linguistics.
• Rico Pérez, Celia, Díez Orzas, Pedro L., Deriard Nolasco, Giuseppe, Cámara, Lidia, Regidor, Igone, Ariano, Martin, Fernández, Félix, Blasco, Johanna, Nieto Caride, Pablo, & Benítez Monje, Roberto. (2011). D4.1.4 Annex I. EDI-TA. Post-editing methodology for machine translation. MultilingualWeb-LT, Language Technology in the Web FP7-ICT-2011-7 (Project Number 287815).
• Rodríguez-Rubio, Santiago, & Fernández-Quesada, Nuria. (2020a). Towards accuracy: A model for the analysis of typographical errors in specialised bilingual dictionaries. Two case studies. Lexikos, 30, 386-415. https://doi.org/10.5788/30-1-1606
• Rodríguez-Rubio, Santiago, & Fernández-Quesada, Nuria. (2020b). The dynamics of typographical error reproduction: Optimising formal correctness in three specialised bilingual dictionaries. ELIA, 20, 147-190. https://doi.org/10.12795/elia.2020.i20.06
• Rodríguez-Rubio Mediavilla, Santiago. (2021). Estudio comparativo de erratas en diccionarios especializados inglés-español en papel de los ámbitos económico-financiero, jurídico e industrial. Una propuesta metodológica [Unpublished doctoral dissertation]. Universidad Pablo de Olavide.
• Rodríguez-Rubio, Santiago. (2022). La imperfección de los diccionarios: estudio teórico sobre la transmisión de las erratas en los últimos siglos. Revista de Lexicografía, 28, 207-222. https://doi.org/10.17979/rlex.2022.28.1.9125
• Rodríguez-Rubio, Santiago. (In press). Did you mean…? Rendimiento de Google Translate y DeepL en la detección y corrección de erratas de un diccionario económico, financiero y comercial (inglés-español). In Ana Medina Reguera (Ed.), Traducción, localización, discurso; textos de la economía y la empresa. Peter Lang.
• Rumelhart, David E., Hinton, Geoffrey E., & Williams, Ronald J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. https://doi.org/10.1038/323533a0
• Sennrich, Rico, Haddow, Barry, & Birch, Alexandra. (2016). Neural machine translation of rare words with subword units. In Katrin Erk, & Noah A. Smith (Eds.), Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 1715-1725). Association for Computational Linguistics.
• Sosoni, Vilelmini, O’Shea, John, & Stasimioti, Maria. (2022). Translating law: A comparison of human and post-edited translations from Greek to English. Revista de Llengua i Dret, Journal of Language and Law, 78, 92-120. http://dx.doi.org/10.2436/rld.i78.2022.3704
• Sun, Zeyu, Zhang, Jie M., Harman, Mark, Papadakis, Mike, & Zhang, Lu. (2020). Automatic testing and improvement of machine translation. In Gregg Rothermel, & Doo-Hwan Bae (Eds.), ICSE ‘20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (pp. 974-985). Association for Computing Machinery.
• Tan, Zhixing, Wang, Shuo, Yang, Zonghan, Chen, Gang, Huang, Xuancheng, Sun, Maosong, & Liu, Yang. (2020). Neural machine translation: A review of methods, resources, and tools. AI Open, 1, 5-21. https://doi.org/10.1016/j.aiopen.2020.11.001
• Varela-Salinas, María José, & Burbat, Ruth. (2023). Google Translate and DeepL: breaking taboos in translator training. Ibérica, 45, 243-266. https://doi.org/10.17398/2340-2784.45.243
• Wahler, Madison E. (2018, January 3). A word is worth a thousand words: Legal implications of relying on machine translation technology. Carlton Fields.
• Weber, Steven, & Mehandru, Nikita. (2022). The 2020s political economy of machine translation. Business and Politics, 24(1), 96-112. https://doi.org/10.1017/bap.2021.17
• Wiesmann, Eva. (2019). Machine translation in the field of law: A study of the translation of Italian legal texts into German. Comparative Legilinguistics, 37, 117-153. https://doi.org/10.14746/cl.2019.37.4
• Wu, Lijun, Tan, Xu, He, Di, Tian, Fei, Qin, Tao, Lai, Jianhuang, & Liu, Tie-Yan. (2018). Beyond error propagation in neural machine translation: Characteristics of language also matter. In Ellen Riloff, David Chiang, Julia Hockenmaier, & Jun’ichi Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3602-3611). Association for Computational Linguistics.
• Yan, Jianhao, Meng, Fandong, & Zhou, Jie. (2022). Probing causes of hallucinations in neural machine translations. Cornell University. https://doi.org/10.48550/arXiv.2206.12529
• Yang, Ziyan, Pinto-Alva, Leticia, Dernoncourt, Franck, & Ordoñez, Vicente. (2022). Backpropagationbased decoding for multimodal machine translation. Frontiers in Artificial Intelligence, 4, Article 736722. https://doi.org/10.3389/frai.2021.736722
• Xue, Haiyang, Feng, Yang, Gu, Shuhao, & Chen, Wei. (2020). Robust neural machine translation with ASR errors. In Hua Wu, Colin Cherry, Liang Huang, Zhongjun He, Mark Liberman, James Cross, & Yang Liu (Eds.), Proceedings of the 1st Workshop on Automatic Simultaneous Translation (pp. 15-23). Association for Computational Linguistics.
• Zhang, Tong, Zhang, Long, Ye, Wei, Li, Bo, Sun, Jinan, Zhu, Xiaoyu, Zhao, Wen, & Zhang, Shikun. (2021). Point, disambiguate and copy: Incorporating bilingual dictionaries for neural machine translation. In Chengqing Zong, Fei Xia, Wenjie Li, & Roberto Navigli (Eds.), Proceedings of the 59th Annual
• Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 3970-3979). Association for Computational Linguistics.
• Zhao, Wei, Wang, Liang, Shen, Kewei, Jia, Ruoyu, & Liu, Jingming. (2019). Improving grammatical error correction via pre-training a copy-augmented architecture with unlabeled data. In Jill Burstein, Christy Doran, & Thamar Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 156-165). Association for Computational Linguistics.