Compartir recursos lingüístics de qualitat en l’àmbit jurídic per desenvolupar la traducció automàtica neuronal per a les llengües europees amb pocs recursos

Petra Bago, Sheila Castilho, Edoardo Celeste, Jane Dunne, Federico Gaspari, Níels Rúnar Gíslason, Andre Kåsen, Filip Klubička, Gauti Kristmannsson, Helen McHugh, Róisín Moran, Órla Ní Loinsigh, Jon Arild Olsen, Carla Parra Escartín, Akshai Ramesh, Natalia Resende, Páraic Sheridan, Andy Way

Resum


En aquest article es presenten algunes de les reeixides principals del projecte PRINCIPLE, finançat per la Unió Europea, en relació amb la recopilació de recursos lingüístics (RL) de qualitat en l’àmbit jurídic per a quatre llengües europees amb pocs recursos: el croat, l’irlandès, el noruec i l’islandès. Després d’il·lustrar la importància d’aquest treball per desenvolupar tecnologies de la traducció en el marc de la Unió Europea i l’Espai Econòmic Europeu, en l’article es descriuen els passos principals per recollir dades, conservar i compartir els RL recopilats amb l’ajuda de proveïdors de dades públics i privats. A continuació, es descriuen el procés de desenvolupament i les característiques clau dels motors de traducció automàtica (TA) neuronal d’última generació adaptats al context jurídic que s’han creat amb aquestes dades. Els sistemes de TA es van avaluar amb una combinació de mètodes automàtics i humans per validar la qualitat dels RL recollits en el projecte i, més endavant, els RL de qualitat es van compartir amb el públic general a través del repositori ELRC-SHARE. L’article debat les dificultats més importants amb què ha topat aquest treball i s’hi subratlla la importància i els avantatges primordials de compartir RL digitals de qualitat.

Paraules clau


recursos lingüístics; llengües amb pocs recursos; traducció jurídica; traducció automàtica neuronal; avaluació

Cites


Bago, Petra, Dunne, Jane, Gaspari, Federico, Kåsen, Andre, Kristmannsson, Gauti, McHugh, Helen, Olsen, Jon Arild, Sheridan, Dana D., Sheridan, Páraic, Tinsley, John, & Way, Andy. (2020). Progress of the PRINCIPLE project: Promoting MT for Croatian, Icelandic, Irish and Norwegian. In André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Turchi Marco, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, & Mikel L. Forcada (eds.), Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (pp. 465–466). European Association for Machine Translation.

Bago, Petra, Castilho, Sheila, Dunne, Jane, Gaspari, Federico, Kåsen, Andre, Kristmannsson, Gauti, Olsen, Jon Arild, Resende, Natalia, Rúnar, Gíslason Níels, Sheridan, Dana D., Sheridan, Páraic, Tinsley, John, & Way, Andy. (2022). Achievements of the PRINCIPLE project: Promoting MT for Croatian, Icelandic, Irish and Norwegian. In Lieve Macken, Andrew Rufener, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne, Loïc Barrault, Marta R. Costa-Jussà, Ellie Kemp, Spyridon Pilos, Christophe Declercq, Maarit Koponen, Mikel L. Forcada, Carolina Scarton, & Helena Moniz (eds.), Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (pp. 349–350). European Association for Machine Translation.

Banerjee Satanjeev, & Lavie, Alon. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Jade Goldstein, Alon Lavie, Chin-Yew Lin, & Clare Voss (eds.), Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization (pp. 65–72). European Association for Machine Translation.

Bojar Ondřej, Graham, Yvette, & Kamran, Amir. (2017). Results of the WMT17 Metrics Shared Task. In Ondřej Bojar, Christian Buck, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, & Julia Kreutzer (Eds.), Proceedings of the Second Conference on Machine Translation (pp. 489–513). European Association for Machine Translation. https://doi.org/10.18653/v1/W17-4755

Bowker Lynne, & Ciro, Jairo Buitrago. (2019). Machine translation and global research: Towards improved machine translation literacy in the scholarly community. Emerald Publishing.

Callison-Burch Chris, Osborne, Miles, & Koehn, Philipp. (2006). Re-evaluating the role of BLEU in machine translation research. In Diana McCarthy, & Shuly Wintner (Eds.), Proceedings of 11th Conference of the European Chapter of the Association for Computational Linguistics (pp. 249–256). European Association for Machine Translation.

Castilho, Sheila, Doherty, Stephen, Gaspari, Federico, & Moorkens, Joss. (2018). Approaches to human and machine translation quality assessment. In Joss Moorkens, Sheila Castilho, Federico Gaspari, & Stephen Doherty (eds.), Translation quality assessment: From principles to practice (pp. 9–38). Springer. https://doi.org/10.1007/978-3-319-91241-7_2

Chen, Boxing, Cherry, Colin, Foster, George, & Larkin, Samuel. (2017). Cost weighting for neural machine translation domain adaptation. In Thang Luong, Alexandra Birch, Graham Neubig, & Andrew Finch (eds.), Proceedings of the First Workshop on Neural Machine Translation (pp. 40–46). Association for Computational Linguistics. https://doi.org/10.18653/v1/W17-3205

Chu, Chenhui, Dabre, Raj, & Kurohashi, Sadao. (2017). An empirical comparison of domain adaptation methods for neural machine translation. In Regina Barzilay, & Min-Yen Kan (Eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 385–391). Association for Computational Linguistics. https://doi.org/10.18653/v1/P17-2061

Chu, Chenhui, & Wang, Rui. (2018). A survey of domain adaptation for neural machine translation. In Emily M. Bender, Leon Derczynski, & Pierre Isabelle (Eds.), Proceedings of the 27th International Conference on Computational Linguistics (pp. 1304–1319). Association for Computational Linguistics.

Chu, Chenhui, Dabre, Raj, & Kurohashi, Sadao. (2018). A comprehensive empirical comparison of domain adaptation methods for neural machine translation. Journal of Information Processing, 26, 529–538. https://doi.org/10.2197/ipsjjip.26.529

Clark, Jonathan H., Dyer, Chris, Lavie, Alon, & Smith, Noah A. (2011). Better hypothesis testing for statistical machine translation: Controlling for optimizer instability. In Dekang Lin, Yuji Matsumoto,

& Rada Mihalcea (Eds.), Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 176–181). Association for Computational Linguistics.

Denkowski, Michael, & Lavie, Alon. (2012). Challenges in predicting machine translation utility for human post-editors. Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers (article 6). Association for Machine Translation in the Americas.

Eide, Kristine, Kåsen, Andre, & Dale, Ingerid Løyning. (2022). D1.26: Report on the Norwegian language. European Language Equality Project.

Etchegoyhen, Thierry, Fernández Torné, Anna, Azpeitia, Andoni, Martínez García, Eva, & Matamala, Anna. (2018). Evaluating domain adaptation for machine translation across scenarios. In Nicoletta Calzolari, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, & Takenobu Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. 6–15). European Language Resources Association.

Gaspari, Federico, Way, Andy, Dunne, Jane, Rehm, Georg, Piperidis, Stelios, & Giagkou, Maria. (2022). D1.1: Digital language equality (preliminary definition). European Language Equality Project.

Gupta, Rohit, Lambert, Patrik, Nath Patel, Raj, & Tinsley, John. (2019). Improving robustness in real-world neural machine translation engines. In Mikel L. Forcada, Andy Way, John Tinsley, Dimitar Shterionov, Celia Rico, & Federico Gaspari (eds.), Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks (pp. 142–148). European Association for Machine Translation.

Kahaner, Steven. (2005). Issues in legal translation. Ccaps Translation and Localization, 1–3.

Klubička, Filip, Kasunić, Lorena, Blazsetin, Danijel, & Bago, Petra. (2022). Challenges of building domainspecific parallel corpora from public administration documents. In Reinhard Rapp, Pierre Zweigenbaum, & Serge Sharoff (Eds.), Proceedings of the 15th Workshop on Building and Using Comparable Corpora (BUCC 2022) within LREC 2022 (pp. 50–55). Association for Computational Linguistics.

Koehn, Philipp, & Ulrich, Germann. (2014). The impact of machine translation quality on human post-editing. In Ulrich Germann, Michael Carl, Philipp Koehn, Germán Sanchis-Trilles, Francisco Casacuberta, Robin Hill, & Sharon O’Brien (eds.), Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation (pp. 38–46). Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-0307

Lavie, Alon, & Agarwal, Abhaya. (2007). METEOR: An automatic metric for MT evaluation with high levels of correlation with human judgments. In Philipp Koehn, & Christof Monz (Eds.), Proceedings of the Workshop on Statistical Machine Translation (pp. 228–231). Association for Computational Linguistics. https://doi.org/10.3115/1626355.1626389

Läubli, Samuel, Amrhein, Chantal, Düggelin, Patrick, Gonzalez, Beatriz, Zwahlen, Alena, & Volk, Martin. (2019). Post-editing productivity with neural machine translation: An empirical assessment of speed and quality in the banking and finance domain. In Mikel Forcada, Andy Way, Barry Haddow, & Rico Sennrich (Eds.), Proceedings of Machine Translation Summit XVII: Research Track (pp. 267–272). Association for Computational Linguistics.

Lommel, Arle, Uszkoreit, Hans, & Burchardt, Aljoscha. (2014). Multidimensional quality metrics (MQM): A framework for declaring and describing translation quality metrics. Revista Tradumàtica: Tecnologies de la Traducció, 12, 455–463. https://doi.org/10.5565/rev/tradumatica.77

Lynn, Teresa. (2022). D1.20: Report on the Irish language. European Language Equality Project.

O’Brien, Sharon, & Ehrensberger-Dow, Maureen. (2020). MT literacy – A cognitive view. Translation, Cognition & Behavior, 3(2), 145–164. https://doi.org/10.1075/tcb.00038.obr

Papineni, Kishore, Roukos, Salim, Ward, Todd, & Zhu, Wei-Jing. (2002). BLEU: A method for automatic evaluation of machine translation. In Pierre Isabelle, Eugene Charniak, & Dekang Lin (eds.), Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (pp. 311–318). Association for Computational Linguistics.

Plitt, Mirko, & Masselot, François. (2010). A productivity test of statistical machine translation post-editing in a typical localisation context. The Prague Bulletin of Mathematical Linguistics, 93, 7–16.

Popović, Maja. (2015). ChrF: Character n-gram F-score for automatic MT evaluation. In Ondřej Bojar, Rajan Chatterjee, Christian Federmann, Barry Haddow, Chris Hokamp, Matthias Huck, Varvara Logacheva, & Pavel Pecina (Eds.), Proceedings of the 10th Workshop on Statistical Machine Translation (WMT–15) (pp. 392–395). Association for Computational Linguistics.

Post, Matt. (2018). A call for clarity in reporting BLEU scores. In Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, & Karin Verspoor (Eds.), Proceedings of the Third Conference on Machine Translation (WMT): Research Papers (pp. 186–191). Association for Computational Linguistics.

Radding, Charles, & Ciaralli, Antonio. (2006). The corpus iuris civilis in the Middle Ages: Manuscripts and transmission from the sixth century to the juristic revival. Brill.

Rögnvaldsson, Eiríkur. (2022). D1.19: Report on the Icelandic language. European Language Equality Project.

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 (Volume 1: Long Papers) (pp. 1715–1725). Association for Computational Linguistics.

Snover, Matthew, Dorr, Bonnie, Schwartz, Richard, Micciulla, Linnea, & Makhoul, John. (2006). A study of translation edit rate with targeted human annotation. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: “Visions for the Future of Machine Translation” (pp. 223–231). Association for Machine Translation in the Americas.

Tadić, Marko. (2022). D1.7: Report on the Croatian language. European Language Equality Project.

Thompson, Brian, & Koehn, Phillip. (2019). Vecalign: Improved sentence alignment in linear time and space. In Sebastian Padó, & Ruihong Huang (eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP–IJCNLP) (pp. 1342–1348). Association for Computational Linguistics.

Varga, Dániel, Németh, László, Halácsy, Péter, Kornai, András, Trón, Viktor, & Nagy, Viktor. (2005). Parallel corpora for medium density languages. In Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, Nicolas Nicolov, & Nikolai Nikolov (Eds.), International conference on recent advances in natural language processing (RANLP) 2005 (pp. 590–596). Association for Computational Linguistics.

Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, & Polosukhin, Illia. (2017). Attention is all you need. In Isabelle Guyon, Ulrike Von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, Shri N. S. Vishwanathan, & Roman Garnett (Eds.), NeurIPS Processing: Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 5998– 6008). Conference on Neural Information Processing Systems.

Way, Andy. (2018). Quality expectations of machine translation. In Joss Moorkens, Sheila Castilho, Federico Gaspari, & Stephen Doherty (eds.), Translation quality assessment: From principles to practice (pp. 159–178). Springer. https://doi.org/10.1007/978-3-319-91241-7_8

Way, Andy, & Gaspari, Federico. (2019). PRINCIPLE: Providing resources in Irish, Norwegian, Croatian and Icelandic for the purposes of language engineering. In Mikel Forcada, Andy Way, John Tinsley, Dimitar Shterionov, Celia Rico, & Federico Gaspari (eds.), Proceedings of Machine Translation Summit XVII, Volume 2: Translator, Project and User Tracks (pp. 112–113). European Association for Machine

Translation.

Way Catherine. (2016). The challenges and opportunities of legal translation and translator training in the 21st century. International Journal of Communication, 10, 1009–1029.

Wolff, Leon. (2011). Legal translation. In Kirsten Malmkjær, & Kevin Windle (eds.), The Oxford handbook of translation studies (pp. 228–242). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199239306.013.0017

Zhang, Mike, & Toral, Antonio. (2019). The effect of translationese in machine translation test sets. In Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, & Karin Verspoor (eds.), Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers) (pp. 73–81). Association for Computational Linguistics.




DOI: http://dx.doi.org/10.2436/rld.i78.2022.3741



 

Reconeixement - NoComercial - SenseObraDerivada (by-nc-nd): No es permet un ús comercial de l'obra original ni la generació d'obres derivades.