Avaluació de l'efectivitat de la traducció automàtica de l'àudiodescripció: els resultats de dos estudis pilot per a la combinació lingüística anglès-holandès
Resum
El camp de la traducció està experimentant canvis profunds. D'una banda, està patint una autèntica transformació gràcies a l'arribada i la millora constant de les noves tecnologies. De l’altra, noves formes de traducció comencen a veure la llum arran de l'evolució social i legal que exigeix que els productes i continguts que es creen siguin accessibles per a tothom. Una d'aquestes noves formes de traducció és l'audiodescripció (AD), un servei que té com a objectiu fer accessibles els continguts audiovisuals a les persones amb pèrdua de visió. La nova legislació exigeix que aquests continguts siguin accessibles abans del 2025, la qual cosa constitueix una tasca immensa atès el nombre limitat de persones que actualment tenen formació com a audiodescriptors. Una possible solució seria utilitzar la traducció automàtica per traduir les audiodescripcions ja existents a diferents idiomes. L’AD, caracteritzada per frases curtes i un llenguatge senzill i concret, podria ser una bona candidata per a la traducció automàtica. En aquest estudi pretenem demostrar la hipòtesi per a la combinació lingüística anglès-holandès. Concretament, fragments de 30 minuts d’AD de tres pel·lícules holandeses que es van audiodescriure originalment en anglès, han estat traduïts a l'holandès per mitjà de l’eina DeepL. Les traduccions s’han analitzat utilitzant la tipologia d'error harmonitzada DQF-MQM i tenint en compte la naturalesa multimodal específica del text font i la dimensió intersemiòtica del procés d’audiodescripció original. L'anàlisi ha mostrat que la producció de TA té una taxa d'error relativament alta, especialment en les categories de precisió –errors de traducció– i fluïdesa gramatical. Això sembla indicar que caldrà una extensa postedició, abans que el text es pugui utilitzar en un context professional.
Paraules clau
accessibilitat als mitjans, audiodescripció, traducció automàtica, traduccióReferències
Álvarez Muniain, A.; Balenciaga, M.; Pozo Echezarreta, A. D.; Arzelus Irazusta, H.; Matamala, A.; Martínez Hinarejos, C. D. (2016). Impact of automatic segmentation on the quality, productivity and self-reported post-editing effort of intralingual subtitles. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association (ELRA), pp. 3049-3053. <https://aclanthology.org/L16-1487/>. [Accessed: 20211116].
Arma, S. (2011). The Language of filmic audio description. PhD dissertation. Napoli: Universita Degli Studi di Napoli Federico II.
Bahdanau, D.; Cho, K. H.; Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. Paper presented at 3rd International Conference on Learning Representations, ICLR 2015, San Diego.
Banerjee, S.; Lavie, A. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization. Association for Computational Linguistics, pp. 65-72. <https://aclanthology.org/W05-0909/>. [Accessed: 20211116].
Bawden, R.; Sennrich, R.; Birch, A.; Haddow, B. (2018). Evaluating discourse phenomena in neural machine translation. In: Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, pp. 1304-1313. <https://doi.org/10.18653/v1/N18-1118>. [Accessed: 20211116].
Bourne, J.; Hurtado, C. (2007). From the Visual to the Verbal in two Languages: a contrastive analysis of the audio description of The Hours in English and Spanish. In: Diaz-Cintas, J.; Orero, P.; Remael, A. (eds.). Media for all: Subtitling for the Deaf, Audio Description, and Sign Language. Amsterdam: Rodopi.
Bryssinck, J. (2019). The (semi) automatic translation of audio descriptions: an exploratory study. [MA thesis]. University of Antwerp.
Bywood, L.; Georgakopoulou, P.; Etchegoyhen, T. (2017). Embracing the threat: machine translation as a solution for subtitling. Perspectives: Studies in Translation Theory and Practice, v. 25, n. 3, pp. 492–508. <https://doi.org/10.1080/0907676X.2017.1291695>. [Accessed: 20211116].
Castilho, S.; Doherty, S.; Gaspari, F.; Moorkens, J. (2018). Approaches to human and machine translation quality assessment. In: Moorkens, J.; Castilho, S.; Gaspari, F.; Doherty, S. (eds.). Translation quality assessment: From principles to practice. Cham: Springer, pp. 9-38. <https://doi.org/10.1007/978-3-319-91241-7_2>. [Accessed: 20211116].
Castilho, S.; Moorkens, J.; Gaspari, F.; Sennrich, R.; Way, A.; Georgakopoulou, P. (2018). Evaluating MT for massive open online courses. Machine Translation, v. 32, n. 3, pp. 255–278. <https://doi.org/10.1007/s10590-018-9221-y>. [Accessed: 20211116].
Cho, K.; Van Merrienboer, B.; Gulcehre, C.; Bougares, F.; Schwenk, H.; Bengio, Y. (2014a). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp. 1724–1734. <https://doi.org/10.3115/v1/D14-1179>. [Accessed: 20211116].
Del Pozo, A. (2014). Sumat: An Online Service for Subtitling by Machine Translation. Sumat website. <http://www.fp7-sumat-project.eu/>. [Accessed: 20211116].
Etchegoyhen, T.; Bywood, L.; Fishel, M.; Georgakopoulou, P.; Jiang, J.; Van Loenhout, G.; Del Pozo, A.; Sepesy Maucec, M.; Turner, A.; Volk, M. (2014). Machine translation for subtitling: A large-scale evaluation. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC14). European Language Resources Association, pp. 46–53. <https://aclanthology.org/L14-1392/>. [Accessed: 20211116].
Fernández-Torné, A. (2016). Machine translation evaluation through post-editing measures in audio description. InTRAlinea: online translation journal v. 18. <https://www.intralinea.org/index.php/archive/article/machine_translation_evaluation_through_post_editing_measures_in_audio_descr>. [Accessed: 20211116].
Fernández-Torné, A.; Matamala, A. (2016). Machine translation in audio description? Comparing creation, translation and post-editing efforts. SKASE Journal of Translation and Interpretation, v. 9, n. 1, pp. 64–87. <http://www.skase.sk/JTI10index.html>. [Accessed: 20211116].
Guerberof-Arenas, A.; Toral, A. (2020). The impact of post-editing and machine translation on creativity and reading experience. Translation Spaces, v. 9, n. 2, pp. 255–282. <https://doi.org/10.1075/ts.20035.gue>. [Accessed: 20211116].
Herrador Molina, D. (2006). La Traducción de guiones de audiodescripción del inglés al español. [Proyecto fin de carrera]. Universidad de Granada.
Jankowska, A. (2015). Translating audio description scripts: translation as a new strategy of creating audio description. London [etc.]: Peter Lang.
Jankowska, A.; Milc, M.; Fryer, L. (2017). Translating audio description scripts… into English. SKASE Journal of Translation and Interpretation, v. 10, n. 2, pp. 2–16. <http://www.skase.sk/JTI13index.html>. [Accessed: 20211116].
Liu, Y.; Tor-Carroggio, I.; Rovira-Esteva, S.; Casas-Tost, H. (2021). Localisation guidelines for translating AD from Spanish into Chinese: A first proposal. [Paper presentation]. Advanced research seminar on audio description, January 26-27.
Lommel, A. (2018). Metrics for translation quality assessment: A case for standardising error typologies. In: Moorkens, J.; Castilho, S.; Gaspari, F.; Doherty, S. (eds.). Translation quality assessment: From principles to practice. Cham: Springer, pp. 109-127. <https://doi.org/10.1007/978-3-319-91241-7_6>. [Accessed: 20211116].
Lopez Vera, J. F. (2006). Translating Audio Description Scripts: The Way Forward?:Tentative First Stage Project Results. In: MuTra 2006: Audiovisual Translation Scenarios Conference Proceedings: Audiovisual Translation Scenarios, pp. 1-10. <https://www.euroconferences.info/proceedings/2006_Proceedings/2006_proceedings.html>. [Accessed: 20211116].
Mälzer, N.; Schaeffer-Lacroix, E. (2021). The French-German TADS project: A multi method approach to the translation of AD scripts [Paper presentation]. Advanced research seminar on audio description, online, January 26-27. <https://grupsderecerca.uab.cat/arsad/node/149>. [Accessed: 20211116].
Macken, L.; Fonteyne, M.; Tezcan, A.; Daems, J. (2020). Assessing the comprehensibility of automatic translations (ArisToCAT). In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (EAMT2020). European Association for Machine Translation, pp. 485-486. <https://aclanthology.org/2020.eamt-1.64/>. [Accessed: 20211116].
Matamala, A.; Ortiz- Boix, C. (2016). Accessibility and multilingualism: an exploratory study on the machine translation of audio descriptions. TRANS, n. 20, pp. 11–24. <https://doi.org/10.24310/TRANS.2016.v0i20.2059>. [Accessed: 20211116].
Matusov, E.; Wilken, P.; Georgakopoulou, P. (2019). Customizing neural machine translation for subtitling. In: Proceedings of the Fourth Conference on Machine Translation: Vol 1., Research Papers. Association for Computational Linguistics, pp. 82-93. <https://doi.org/10.18653/v1/W19-5209>. [Accessed: 20211116].
O’Hagan, M. (2019). The Routledge Handbook of Translation and Technology. London [etc.]: Routledge. <https://doi.org/10.4324/9781315311258>. [Accessed: 20211116].
O’Hagan, M. (2020). Introduction: Translation and technology: disruptive entanglement of human and machine. In: O’Hagan, M. (ed.). The Routledge Handbook of Translation and Technology. London [etc.]: Routledge, pp. 1-8. <https://doi.org/10.4324/9781315311258-1>. [Accessed: 20211116].
Papineni, K.; Roukos, S.; Ward, T.; Zhu, W. J. (2002). BLEU: a method for automatic evaluation of machine translation. In: ACL’02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. <https://doi.org/10.3115/1073083.1073135>. [Accessed: 20211116].
Pedersen, J. (2008). High felicity: A speech act approach to quality assessment in subtitling. In: Chiaro, D.; Heiss, C.; Bucaria C. Between text and image: updating research in screen translation. Amsterdam: John Benjamins, pp. 101-115. <https://doi.org/10.1075/btl.78.11ped>. [Accessed: 20211116].
Popović, M. (2015). chrF: character n-gram F-score for automatic MT evaluation. In: Proceedings of the Tenth Workshop on Statistical Machine Translation. Association for Computational Linguistics, pp. 392-395. <https://doi.org/10.18653/v1/W15-3049>. [Accessed: 20211116].
Popović, M. (2018). Error classification and analysis for machine translation quality assessment. In: Moorkens, J.; Castilho, S.; Gaspari, F.; Doherty, S. (eds.). Translation quality assessment: From principles to practice. Cham: Springer, pp. 129-158. <https://doi.org/10.1007/978-3-319-91241-7_7>. [Accessed: 20211116].
Remael, A.; Vercauteren, G. (2010). The translation of recorded audio description from English into Dutch. Perspectives: Studies in Translation Theory and Practice, v. 18, n. 3, pp. 155–171. <https://doi.org/10.1080/0907676X.2010.485684>. [Accessed: 20211116].
Reviers, N. (2016). Audio description services in Europe: an update. JoSTrans, The Journal of Specialised Translation, n. 26, pp. 232-247. <https://www.jostrans.org/issue26/art_reviers.pdf>. [Accessed: 20211116].
Reviers, N. (2018a). Audio description in Dutch: A corpus-based study into the linguistic features of a new, multimodal text type. (PhD Book). University of Antwerp. <https://explore.lib.uliege.be/discovery/fulldisplay/alma9919943602602321/32ULG_INST:MOSA>. [Accessed: 20211116].
Reviers, N. (2018b). Tracking multimodal cohesion in Audio Description: Examples from a Dutch audio description corpus. Linguistica Antverpiensia, New Series: Themes in Translation Studies, v. 17, pp. 22-35. <https://doi.org/10.52034/lanstts.v17i0>. [Accessed: 20211116].
Salway, A. (2004). AuDesc System Specification and Prototypes. TIWO: Television in Words. University of Surrey. <https://andrewsalway.files.wordpress.com/2020/02/tiwo_television_in_words_deliverable_3-1.pdf>. [Accessed: 20211116].
Salway, A. (2007). A corpus based analysis of audio description. In: Diaz-Cintas, J.; Orero, P.; Remael, A. (eds.). Media for all: Subtitling for the deaf, Audio Description and Sign Language. Amsterdam [etc.]: Rodopi, pp. 151-174. <https://doi.org/10.1163/9789401209564_012>. [Accessed: 20211116].
Shterionov, D.; Superbo, R.; Nagle, P.; Casanellas, L.; O’Dowd, T.; Way, A. (2018). Human versus automatic quality evaluation of NMT and PBSMT. Machine Translation, v. 32, n. 3, pp. 217–235. <https://doi.org/10.1007/s10590-018-9220-z>. [Accessed: 20211116].
Sulubacak, U.; Caglayan, O.; Grönroos, S.A.; Rouhe, A.; Elliot, D.; Specia, L.; Tiedemann, J. (2020). Multimodal machine translation through visuals and speech. Machine Translation, v. 34, pp. 97–147. <https://doi.org/10.1007/s10590-020-09250-0>. [Accessed: 20211116].
TAUS. (2021). Harmonized DQF-MQM Error Typology. <https://www.taus.net/qt21-project#harmonized-error-typology>. [Accessed: 20211116].
Tiedemann, J.; Scherrer, Y. (2017). Neural machine translation with extended context. In: Proceedings of the Third Workshop on Discourse in Machine Translation (DISCOMT’17). Association for Computational Linguistics, pp. 82-92. <https://doi.org/10.18653/v1/W17- 4811>. [Accessed: 20211116].
Toral, A.; Way, A. (2018). What level of quality can neural machine translation attain on literary text? In: Moorkens, J.; Castilho, S.; Gaspari, F.; Doherty, S. (eds.). Translation quality assessment: From principles to practice. Cham: Springer, pp. 263-287. <https://doi.org/10.1007/978-3-319-91241-7_12>. [Accessed: 20211116].
Uiterwijk, G. (2019). The (semi) automatic translation of audio descriptions: an exploratory study. [MA thesis] University of Antwerp.
Vercauteren, G. (2007). Towards a European guideline for audio description. In: Díaz-Cintas, J.; Orero, P.; Remael, A. (dds.). Media for All: Subtitling for the Deaf, Audio Description, and Sign Language (pp. 139–150). Amsterdam: Rodopi, pp. 139-150. <https://doi.org/10.1163/9789401209564_011>. [Accessed: 20211116].
Voita, E.; Serdyukov, P.; Sennrich, R.; Titov, I. (2018). Context-aware neural machine translation learns anaphora resolution. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers). Association for Computational Linguistics, pp. 1264-1274. <https://doi.org/10.18653/v1/P18-1117>. [Accessed: 20211116].
Volk, M.; Sennrich, R.; Hardmeier, C.; Tidström, F. (2010). Machine translation of TV subtitles for large scale production. Paper presented at the 2nd joint EM+/CNGL Workshop Bringing MT to the user: research on integrating MT in the translation industry. Denver, Colorado.
Publicades
Descàrregues
Drets d'autor (c) 2021 Gert Vercauteren, Nin Reviers, Kim Steyaert
Aquesta obra està sota una llicència internacional Creative Commons Reconeixement 4.0.
