口述影像机器翻译之有效性评估：英-荷翻译的两个实验性研究

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.