Traducció automàtica augmentada i centrada en les persones: anàlisi de l’experiència d’usuari, la qualitat i la productivitat en la postedició interactiva en comparació amb la postedició tradicional
Resum
Els avenços recents en tecnologies del llenguatge han alterat la professió de la traducció. Tradicionalment, tant la recerca com la indústria s’han centrat en l’ús de més potència computacional i en l’entrenament de models lingüístics més grans, sovint passant per alt els usuaris d’aquestes tecnologies. Fins ara, l’objectiu del desenvolupament tecnològic ha estat la creació d’un agent intel·ligent que emuli el comportament humà per augmentar l’automatització. Com a resposta, recentment ha guanyat presència un nou marc de disseny tecnològic: la intel·ligència artificial centrada en les persones (HCAI), en què, en lloc de substituir els humans, l’objectiu és alinear el desenvolupament de les eines amb els valors, les preferències i les necessitats dels usuaris i, alhora, augmentar-ne les capacitats i millorar-ne el rendiment. Si s’aplica a la traducció automàtica (TA), podem parlar de traducció automàtica augmentada i centrada en les persones (HCAMT). Aquest canvi, que passa de l’emulació a l’empoderament, situa les persones al centre de la IA i de les tecnologies del llenguatge. Aquest article considera l’anàlisi de l’experiència d’usuari en traducció automàtica (MTUX) com una via per fomentar la HCAMT. Per demostrar-ho, duem a terme un estudi longitudinal amb 11 traductors professionals en la combinació lingüística anglès–espanyol per analitzar els efectes de la postedició tradicional (TPE) i de la postedició interactiva (IPE) sobre l’MTUX, la qualitat de la traducció i la productivitat. Els resultats de MTUX suggereixen que els traductors prefereixen la IPE a la TPE perquè senten que tenen més control sobre la interacció en aquesta forma més recent d’interacció traductor–ordinador i se senten més empoderats en la seva relació amb la TA. Els resultats de productivitat també indiquen que els traductors que treballen amb IPE informen d’una productivitat significativament més alta, a efectes estadístics, que quan treballen amb TPE. Pel que fa a la qualitat, els resultats indiquen igualment que els traductors ofereixen traduccions més fluides amb IPE i traduccions igualment adequades en ambdues modalitats de postedició. Tots aquests resultats permeten reflexionar sobre la possible adopció de la IPE com una modalitat de TA per a la postedició més centrada en les persones, que empodera els usuaris, cada vegada més reticents a interactuar amb la postedició de TA en els fluxos de treball de la indústria. L’article també estableix les bases per explorar la HCAMT entre usuaris de TA més enllà dels traductors professionals, obrint la porta a una recerca en TA centrada en l’usuari més inclusiva i diversa.
Paraules clau
IA centrada en humans, experiència d'usuari, tecnologies de la traducció, interacció humà-ordinador, postedició, potenciacióReferències
Alabau, Vicent; Bonk, Ragnar; Buck, Christian; et al. (2013). CASMACAT: An open source workbench for advanced computer aided translation. Prague Bulletin of Mathematical Linguistics, v. 100, n. 1, pp. 101-112. DOI <10.2478/pralin-2013-0016>. [Accessed: 20251219].
Alabau, Vicent; Carl, Michael; García-Martínez, Mercedes; González-Rubio, Jesús. (2016). Learning Advanced Post-Editing. In: Carl, Michael; Bangalore, Srinivas; Schaeffer, Moritz. (eds.). New Directions in Empirical Translation Process Research. Cham: Springer, pp 95–110. <https://doi.org/10.1007/978-3-319-20358-4_5>. [Accessed: 20251219].
Albarracín, Dolores (2021). The Impact of Past Experience and Past Behavior on Attitudes and Behavior. In: Action and Inaction in a Social World: Predicting and Changing Attitudes and Behavior. Cambridge: Cambrigde University Press. <https://doi.org/10.1017/9781108878357.006>. [Accessed: 20251219].
Albarracín, Dolores; Wyer, Robert S. (2000). The Cognitive Impact of Past Behavior: Influences on Beliefs, Attitudes, and Future Behavioral Decisions. Journal of Personality and Social Psychology, v. 79, n. 1, pp. 5-22. <https://doi.org/10.1037//0022-3514.79.1.5>. [Accessed: 20251219].
Alves, Fabio; Sarto Szpak, Karina; Gonçalves; José Luiz; et al. (2016). Investigating Cognitive Effort in Post-Editing: A Relevance-Theoretical Approach. In: Hansen-Schirra, Silvia; Grucza, Sambor (eds.). Eyetracking and Applied Linguistics. Berlin: Language Science Press, pp. 109-141. <http://langsci-press.org/catalog/book/108>. [Accessed: 20251219].
Artstein, Ron. (2017). Inter-Annotator Agreement. In: Ide, Nancy; Pustejovsky, James (eds.). Handbook of Linguistic Annotation. Dordrecht: Springer Netherlands, pp. 297-313. <https://doi.org/10.1007/978-94-024-0881-2_11>. [Accessed: 20251219].
Artstein, Ron; Poesio, Massimo (2008). Inter-Coder Agreement for Computational Linguistics. Computational Linguistics, v. 34, n. 4, pp. 555-96. <https://doi.org/10.1162/coli.07-034-R2>. [Accessed: 20251219].
Bowker, Lynne. (2023). De-Mystifying Translation: Introducing Translation to Non-Translators. London; New York: Rouledge. <https://doi.org/10.4324/9781003217718>. [Accessed: 20251219].
Briva-Iglesias, Vicent. (2024). Fostering Human-Centered, Augmented Machine Translation: Analysing Interactive Post-Editing. [PhD Thesis], Dublin City University. Dublin. <https://doras.dcu.ie/30182/>. [Accessed: 20251219].
Briva-Iglesias, Vicent; O’Brien, Sharon (2023). Measuring Machine Translation User Experience (MTUX): A Comparison between AttrakDiff and User Experience Questionnaire. In: Nurminen, Mary; Brenner, Judith; Koponen, Maarit; et al. (eds.). Proceedings of the 24th Annual Conference of the European Association for Machine Translation. European Association for Machine Translation, pp. 335–344. <https://aclanthology.org/2023.eamt-1.33/>. [Accessed: 20251219].
Briva-Iglesias, Vicent; O’Brien, Sharon. (2024). Pre-Task Perceptions of MT Influence Quality and Productivity: The Importance of Better Translator-Computer Interactions and Implications for Training. In: Scarton, Carolina; Prescott, Charlotte; Bayliss, Chris; et al. (eds.). Proceedings of the 25th Annual Conference of the European Association for Machine Translation. European Association for Machine Translation, pp. 444–454. <https://aclanthology.org/2024.eamt-1.37/>. [Accessed: 20251219].
Briva-Iglesias, Vicent; O’Brien, Sharon; Cowan, Benjamin R. (2023). The Impact of Traditional and Interactive Post-Editing on Machine Translation User Experience, Quality, and Productivity. Translation, Cognition & Behavior, v. 6, n. 1, pp. 60-86. <https://doi.org/10.1075/tcb.00077.bri>. [Accessed: 20251219].
Briva-Iglesias, Vicent; Peñuelas Gil, Isabel (2025). Simplifying Healthcare Communication: Evaluating AI-Driven Plain Language Editing of Informed Consent Forms. In: Rivas Ginel, María Isabel; Cadwell, Patrick; Canavese, Paolo; Hansen-Schirra, Silvia; Kappus, Martin; Matamala, Anna; Noonan, Will (eds.). Proceedings of the 1st Workshop on Artificial Intelligence and Easy and Plain Language in Institutional Contexts (AI & EL/PL). European Association for Machine Translation, pp. 55–65. <https://aclanthology.org/2025.aielpl-1.6/>. [Accessed: 20251219].
Brown, Tom B.; Mann, Benjamin; Ryder, Nick; et al. (2020). Language Models Are Few-Shot Learners. arXiv:2005.14165. Preprint. <https://doi.org/10.48550/arXiv.2005.14165>. [Accessed: 20251219].
Cadwell, Patrick; O’Brien, Sharon; Teixeira, Carlos S.C. (2018). Resistance and Accommodation: Factors for the (Non-) Adoption of Machine Translation among Professional Translators. Perspectives, v. 26, n. 3, pp. 301-321. <https://doi.org/10.1080/0907676X.2017.1337210>. [Accessed: 20251219].
Carl, Michael; Bangalore, Srinivas; Schaeffer, Moritz (eds.) (2016). New Directions in Empirical Translation Process Research: Exploring the CRITT TPR-DB. Cham: Springer International Publishing. <https://doi.org/10.1007/978-3-319-20358-4>. [Accessed: 20251219].
Carpuat, Marine; Asscher, Omri; Bali, Kalika; et al. (2025). An Interdisciplinary Approach to Human-Centered Machine Translation. arXiv:2506.13468. Preprint. <https://doi.org/10.48550/arXiv.2506.13468>. [Accessed: 20251219].
Caruana, Edward Joseph; Roman, Marius; Hernández-Sánchez, Jules; Solli, Piergiorgio (2015). Longitudinal Studies. Journal of Thoracic Disease, v. 7, n. 11, pp. E537-40. <https://doi.org/10.3978/j.issn.2072-1439.2015.10.63>. [Accessed: 20251219].
Castilho, Sheila (2016). Measuring Acceptability of Machine Translated Enterprise Content [PhD Thesis]. Dublin City University. Dublin. <http://doras.dcu.ie/21342/>. [Accessed: 20251219].
Castilho, Sheila. (2021). Towards Document-Level Human MT Evaluation: On the Issues of Annotator Agreement, Effort and Misevaluation. In: Castilho, Sheila; et al. Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval), pp. 34-45. <https://aclanthology.org/2021.humeval-1.4>. [Accessed: 20251219].
Daems, Joke; Macken, Lieve (2019). Interactive Adaptive SMT versus Interactive Adaptive NMT: A User Experience Evaluation. Machine Translation, v. 33, n. 1, pp. 1. <https://doi.org/10.1007/s10590-019-09230-z>. [Accessed: 20251219].
Diggle, Peter J.; et al. (2002). Analysis of Longitudinal Data. 2nd ed. Oxford; New York: Oxford University Press.
Doherty, Stephen; O’Brien, Sharon (2014). Assessing the Usability of Raw Machine Translated Output: A User-Centered Study Using Eye Tracking. International Journal of Human-Computer Interaction, v. 30, n. 1, pp. 40-51. <https://doi.org/10.1080/10447318.2013.802199>. [Accessed: 20251219].
ELIS Research. (2023). European Language Industry Survey 2023: Trends, expectations and concerns of the European language industry. European Association of Translation Companies (EUATC). <https://elis-survey.org/wp-content/uploads/2023/03/ELIS-2023-report.pdf>. [Accessed: 20251219].
ELIS Research. (2025). European Language Industry Survey 2025. European Association of Translation Companies (EUATC). <https://elis-survey.org/wp-content/uploads/2025/03/ELIS-2025_Report.pdf>. [Accessed: 20251219].
European Union (2024). Artificial Intelligence Act (AI Act). <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206>. [Accessed: 20251219].
Fırat, Gökhan. (2021). Uberization of Translation: Impacts on Working Conditions. The Journal of Internationalization and Localization, v. 8, n. 1, pp. 48-75. <https://doi.org/10.1075/jial.20006.fir>. [Accessed: 20251219].
Forcada, Mikel L. (2017). Making Sense of Neural Machine Translation. Translation Spaces, v. 6, n. 2, pp. 2. <https://doi.org/10.1075/ts.6.2.06for>. [Accessed: 20251219].
Foster, George; Isabelle, Pierre; Plamondon, Pierre. (1997). Target-Text Mediated Interactive Machine Translation. Machine Translation, v. 12, pp. 175–194. <https://doi.org/10.1023/A:1007999327580>. [Accessed: 20251219].
Green, Spence (2016). Interactive Machine Translation. Conferences of the Association for Machine Translation in the Americas 93.
Guerberof Arenas, Ana. (2008). Productivity and Quality in the Post-Editing of Outputs from Translation Memories and Machine Translation. The International Journal of Localisation, v. 7, n. 1, pp. 11-21.
Guerberof Arenas, Ana; Moorkens, Joss; O’Brien, Sharon (2021). The Impact of Translation Modality on User Experience: An Eye-Tracking Study of the Microsoft Word User Interface. Machine Translation, v. 35, n. 2, pp. 205-37. <https://doi.org/10.1007/s10590-021-09267-z>. [Accessed: 20251219].
Hassenzahl, Marc. (2008). User Experience (UX): Towards an Experiential Perspective on Product Quality. In: Proceedings of the 20th Conference on l’Interaction Homme-Machine (New York, NY, USA), IHM ’08, September 2, 11-15. <https://doi.org/10.1145/1512714.1512717>. [Accessed: 20251219].
ISO. (2018). ISO 9241-11:2018(En), Ergonomics of Human-System Interaction: Part 11: Usability: Definitions and Concepts. <https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en>. [Accessed: 20251219].
Jiménez-Crespo, Miguel A.; Rodríguez, Stephanie A. (2025). Is It AI, MT or PE That Worry Professionals: Results from a Human-Centered AI Survey. In: Bouillon, Pierrette; Gerlach, Johanna; Girletti, Sabrina; et al. (eds.). Proceedings of Machine Translation Summit XX Volume 1. European Association for Machine Translation, pp. 407-419.<https://aclanthology.org/2025.mtsummit-1.32/>. [Accessed: 20251219].
Karakanta, Alina; Bentivogli, Luisa; Cettolo, Mauro; Negri, Matteo; Turchi, Marco (2022). Post-Editing in Automatic Subtitling: A Subtitlers’ Perspective. In: Monizz, Helena; et al. (eds.). Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, June, 261-70. European Association for Machine Translation. <https://aclanthology.org/2022.eamt-1.29>. [Accessed: 20251219].
Koehn, Philipp. (2009). A Process Study of Computer-Aided Translation. Machine Translation, v. 23, n. 4, pp. 4. <https://doi.org/10.1007/s10590-010-9076-3>. [Accessed: 20251219].
Koponen, Maarit; Sulubacak, Umut; Vitikainen, Kaisa; Tiedemann, Jörg (2020). MT for Subtitling: Investigating Professional Translators’ User Experience and Feedback. In: . Ortega, John E.; Federico, Marcello; Orasan, Constantin; Popovic, Maja (eds.). Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation. Association for Machine Translation in the Americas. <https://aclanthology.org/2020.amta-pemdt.6>. [Accessed: 20251219].
Kovacs, Geza. (2020). Predictive Translation Memory in the Wild: A Study of Interactive Machine Translation Use on Lilt. In: O'Brien, Sharon; Simard, Michael (eds.). Workshop on the Impact of Machine Translation (iMpacT 2020). Association for Machine Translation in the Americas, pp. 152–216. <https://aclanthology.org/2020.amta-impact.7/>. [Accessed: 20251219].
Langlais, Philippe, Foster, George; Lapalme, Guy (2000a). TransType: A Computer-Aided Translation Typing System. ANLP-NAACL 2000 Workshop: Embedded Machine Translation Systems. <https://www.aclweb.org/anthology/W00-0507>. [Accessed: 20251219].
Langlais, Philippe; Foster, George; Lapalme, Guy (2000b). Unit Completion for a Computer-Aided Translation Typing System. In: Sixth Applied Natural Language Processing Conference. <https://doi.org/10.3115/974147.974166>. [Accessed: 20251219].
Läubli, Samuel; Green, Spence (2019). Translation Technology Research and Human-Computer Interaction. In: O’Hagan, Minako (eds.). The Routledge Handbook of Translation and Technology. London: Routledge. <https://doi.org/10.4324/9781315311258>. [Accessed: 20251219].
Laugwitz, Bettina; Held, Theo; Schrepp, Martin. (2008). Construction and Evaluation of a User Experience Questionnaire. International. Journal of Interactive Multimedia and Artificial Intelligence, v. 4, n. 4, pp. 76. <https://doi.org/10.1007/978-3-540-89350-9_6>. [Accessed: 20251219].
Macías, Lorena Pérez. (2020). What Do Translators Think About Post-Editing? : A Mixed-Methods Study of Translators’ Fears, Worries and Preferences on Machine Translation Post-Editing. Revista Tradumàtica: tecnologies de la traducció, n. 18, pp. 11-32. <https://doi.org/10.5565/rev/tradumatica.227>. [Accessed: 20251219].
Macklovitch, Elliott. (2006). TransType2: The Last Word. In: Calzolari, Nicoletta; Choukri, Khalid; Gangemi, Aldo; Maegaard, Bente; Mariani, Joseph; Odijk, Jan; Tapias, Daniel (eds.). Proceedings of the 5th Edition of the International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA). <http://www.lrec-conf.org/proceedings/lrec2006/pdf/14_pdf.pdf>. [Accessed: 20251219].
Moorkens, Joss. (2020). ‘A Tiny Cog in a Large Machine’: Digital Taylorism in the Translation Industry. Translation Spaces, v. 9, n. 1, pp. 12-34. <https://doi.org/10.1075/ts.00019.moo>. [Accessed: 20251219].
Moorkens, Joss. (2023). ‘I Am Not a Number’: On Quantification and Algorithmic Norms in Translation. Perspectives, v. 32, n. 3, pp. 477-492. <https://doi.org/10.1080/0907676X.2023.2278536>. [Accessed: 20251219].
Nurminen, Mary. (2021). Investigating the Influence of Context in the Use and Reception of Raw Machine Translation [Doctoral thesis]. Tampere University. Tampere. <https://researchportal.tuni.fi/en/publications/investigating-the-influence-of-context-in-the-use-and-reception-o>. [Accessed: 20251219].
O’Brien, Sharon (2006). Pauses as Indicators of Cognitive Effort in Post-Editing Machine Translation Output. Across Languages and Cultures, v. 7, n. 1, pp. 1. <https://doi.org/10.1556/Acr.7.2006.1.1>. [Accessed: 20251219].
O’Brien, Sharon (2022). How to Deal with Errors in Machine Translation: Post-Editing. Machine Translation for Everyone. In: Dorothy Kenny (ed.). Machine translation for everyone: Empowering users in the age of artificial intelligence. Berlin: Language Science Press, pp. 105-120. <https://zenodo.org/record/6759982/files/342-Kenny-2022-6.pdf?download=1>. [Accessed: 20251219].
O’Brien, Sharon. (2023). Human-Centered Augmented Translation: Against Antagonistic Dualisms. Perspectives, v. 32, n. 3, pp. 391-406. <https://doi.org/10.1080/0907676X.2023.2247423>. [Accessed: 20251219].
Olohan, Maeve. (2017). Knowing in Translation Practice: A Practice-Theoretical Perspective. Translation Spaces, v. 6, n. 1, pp. 159-80. <https://doi.org/10.1075/ts.6.1.08olo>. [Accessed: 20251219].
Pérez-Ortiz, Juan Antonio; Forcada, Mikel L.; Sánchez-Martínez, Felipe (2022). How Neural Machine Translation Works. In: Kenny, Dorothy (ed.). Machine translation for everyone: Empowering users in the age of artificial intelligence. Berlin: Language Science Press, pp. 141-164. <https://zenodo.org/record/6760020/files/342-Kenny-2022-8.pdf?download=1>. [Accessed: 20251219].
Rossetti, Alessandra (2019). Simplifying, Reading, and Machine Translating Health Content: An Empirical Investigation of Usability [PhD Thesis]. Dublin City University. Dublin. <http://doras.dcu.ie/23124/>. [Accessed: 20251219].
Rossi, Caroline; Carré, Alice (2022). How to Choose a Suitable NMT Solution?: Evaluation of MT Quality. In: Kenny, Dorothy (eds.). Machine translation for everyone: Empowering users in the age of artificial intelligence. Dublin: Language Science Press, pp. 51-79. <https://zenodo.org/record/6759978/files/342-Kenny-2022-4.pdf?download=1>. [Accessed: 20251219].
Sánchez Torrón, Marina (2017). Productivity in Post-Editing and in Neural Interactive Translation Prediction: A Study of English-to-Spanish Professional Translators [PhD Thesis]. University of Auckland. Auckland
Sanchis-Trilles, Germán; Alabau, Vicent; Buck, Christian; et al. (2014). Interactive Translation Prediction versus Conventional Post-Editing in Practice: A Study with the CasMaCat Workbench. Machine Translation, v. 28, n. 3, pp. 217-35. <https://doi.org/10.1007/s10590-014-9157-9>. [Accessed: 20251219].
Schmager, Stefan; Pappas, Ilias O.; Vassilakopoulou, Polyxeni (2025). Understanding Human-Centred AI: A Review of Its Defining Elements and a Research Agenda. Behaviour & Information Technology, v. 44, n. 15, pp. 3771-3810. <https://doi.org/10.1080/0144929X.2024.2448719>. [Accessed: 20251219].
Schmidtke, Dag; Groves, Declan. (2019). Automatic Translation for Software with Safe Velocity. In: Forcada, Mikel; Way, Andy; Tinsley, John; Shterionov, Dimitar; Rico, Celia; Gaspari, Federico (eds.). Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks. European Association for Machine Translation, pp. 159–166. <https://aclanthology.org/W19-6729>. [Accessed: 20251219].
Schrepp, Martin; Hinderks, Andreas; Thomaschewski, Jörg (2014). Applying the User Experience Questionnaire (UEQ) in Different Evaluation Scenarios. In: Marcus, Aaron (ed.). Design, User Experience, and Usability. Theories, Methods, and Tools for Designing the User Experience: Thrid International Conference DUXU 2014. Cham: Springer, pp. 383-392. <https://doi.org/10.1007/978-3-319-07668-3_37>. [Accessed: 20251219].
Schrepp, Martin; Thomaschewski, Jörg; Hinderks, Andreas (2017). Construction of a Benchmark for the User Experience Questionnaire (UEQ). International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), v. 4, n. 4. <https://doi.org/10.9781/ijimai.2017.445>. [Accessed: 20251219].
Shneiderman, Ben. (2022a). Human-Centered AI. Oxford: Oxford University Press. <https://academic.oup.com/book/41126>. [Accessed: 20251219].
Shneiderman, Ben. (2022b). Human-Centered AI: Ensuring Human Control While Increasing Automation. In: Proceedings of the 5th Workshop on Human Factors in Hypertext (HUMAN '22). Association for Computing Machinery. <https://doi.org/10.1145/3538882.3542790>. [Accessed: 20251219].
Torres-Hostench, Olga; Moorkens, Joss; O’Brien, Sharon; Vreeke, Joris (2017). Testing Interaction with a Mobile MT Post-Editing App. Translation & Interpreting: The International Journal of Translation and Interpreting Research, v. 9, n. 2, pp. 138-50. <https://www.trans-int.org/index.php/transint/article/view/645>. [Accessed: 20251219].
Torres-Hostench, Olga; Presas, Marisa; Cid-Leal, Pilar (coords.). (2016). L’ús de Traducció Automàtica i Postedició a les Empreses de Serveis Lingüístics de l’Estat Espanyol: Informe de recerca Projecta 2015. <https://ddd.uab.cat/record/166753>. [Accessed: 20251219].
Vallor, Shannon. (2024). Defining Human-Centered AI: An Interview with Shannon Vallor. In: Régis, Catherine; Denis, Jean-Louis; Axente, María Luciana; Kishimoto, Atsuo. Human-Centered AI: A multidisciplinary Perspective for Policy-Makers, Auditors and users. 1st ed. Boca Raton: Chapman and Hall/CRC, pp. 13-20 <https://doi.org/10.1201/9781003320791-3>. [Accessed: 20251219].
Vanroy, Bram; Tezcan, Arda; Macken, Lieve (2023). MATEO: MAchine Translation Evaluation Online. In: Nurminen, Mary; Brenner, Judith; Koponen, Maarit; et al. (eds.). Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pp. 499-500. <https://aclanthology.org/2023.eamt-1.52/>. [Accessed: 20251219].
Vieira, Lucas Nunes; O’Hagan, Minako; O’Sullivan, Carol (2021). Understanding the Societal Impacts of Machine Translation: A Critical Review of the Literature on Medical and Legal Use Cases. Information, Communication & Society, v. 24, n. 11, pp. 1515-32. <https://doi.org/10.1080/1369118X.2020.1776370>. [Accessed: 20251219].
Wang, Xiangling; Wang, Tingting; Martín, Ricardo Muñoz and Yanfang Jia (2021). Investigating Usability in Postediting Neural Machine Translation: Evidence from Translation Trainees’ Self-Perception and Performance. Across Languages and Cultures, v. 22, n. 1, pp. 100-123. <https://doi.org/10.1556/084.2021.00006>. [Accessed: 20251219].
Winner, Langdon (2007). Do Artifacts Have Politics? In: Weckert, John (ed.). Computer Ethics. 1st ed. Routledge. <https://www.taylorfrancis.com/chapters/edit/10.4324/9781315259697-21/artifacts-politics-langdon-winner>. [Accessed: 20251219].
Zhong, Junhao; Zhong, Yilin; Han, Minghui; Yang, Tianjian; Zhang, Qinghua (2023). The Impact of AI on Carbon Emissions: Evidence from 66 Countries. Applied Economics, v. 56, n. 25, pp. 2975-2989. <https://doi.org/10.1080/00036846.2023.2203461>. [Accessed: 20251219].
Publicades
Com citar
Descàrregues
Drets d'autor (c) 2025 Vicent Briva-Iglesias

Aquesta obra està sota una llicència internacional Creative Commons Reconeixement 4.0.