Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-g4j75 Total loading time: 0 Render date: 2025-01-11T16:02:07.040Z Has data issue: false hasContentIssue false

11 - Exploiting Data-Driven Hybrid Approaches to Translation in the EXPERT Project

Published online by Cambridge University Press:  10 June 2019

By
Constantin Orăsan ,
Carla Parra Escartín ,
Lianet Sepúlveda Torres  and
Eduard Barbu
Edited by
Meng Ji  and
Michael Oakes
Meng Ji
Affiliation:
University of Sydney
Michael Oakes
Affiliation:
University of Wolverhampton
Get access

Summary

Technologies have transformed the way we work, and this is also applicable to the translation industry. In the past thirty to thirty-five years, professional translators have experienced an increased technification of their work. Barely thirty years ago, a professional translator would not have received a translation assignment attached to an e-mail or via an FTP and yet, for the younger generation of professional translators, receiving an assignment by electronic means is the only reality they know. In addition, as pointed out in several works such as Folaron (2010) and Kenny (2011), professional translators now have a myriad of tools available to use in the translation process.

Type
Chapter
Information
Advances in Empirical Translation Studies
Developing Translation Resources and Technologies
 , pp. 198 - 216
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barbu, Eduard (2015). Spotting false translation segments in translation memories. In Proceedings of the Workshop Natural Language Processing for Translation Memories. Association for Computational Linguistics. Hissar, Bulgaria, September 2015, pp. 9–16. www.aclweb.org/anthology/W15-5202.Google Scholar
Barbu, Eduard (2017). Ensembles of classifiers for cleaning web parallel corpora and translation memories. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, Varna, Bulgaria, September 2017, pp. 71–77. https://doi.org/10.26615/978–954-452–049-6_011.CrossRefGoogle Scholar
Barbu, Eduard, Parra Escartín, Carla, Bentivogli, Luisa, Negri, Matteo, Turchi, Marco, Orăsan, Constantin and Federico, Marcello (2016). The first Automatic Translation Memory Cleaning Shared Task. Machine Translation, 30 (3–4) (December 2016), 145–166. ISSN 0922–6567. DOI:10.1007/s10590-016-9183-http://link.springer.com/10.1007/s10590-016–9183-x.CrossRefGoogle Scholar
Costa, Hernani, Pastor, Gloria Corpas, Seghiri, Miriam and Mitkov, Ruslan (2015). Towards a Web-based tool to semi-automatically compile, manage and explore comparable and parallel corpora. In New Horizons in Translation and Interpreting Studies (Full papers), December 2015, 133–141.Google Scholar
Cuong, Hoang, Sima’an, Khalil and Titov, Ivan (2016). Adapting to all domains at once: Rewarding domain invariance in SMT. Transactions of the Association for Computational Linguistics 4, 99–112. https://transacl.org/ojs/index.php/tacl/article/view/768.Google Scholar
Daiber, Joachim and Sima’an, Khalil (2015). Machine translation with source-predicted target morphology. In Proceedings of MT Summit XV. Miami, Florida, 2015, pp. 283–296.Google Scholar
Folaron, Deborah (2010). Translation tools. In Gambier, Yves and van Doorslaer, Luc (eds.), Handbook of Translation Studies, vol. 1, Amsterdam; Philadelphia: John Benjamins Publishing Co., pp. 429–436.Google Scholar
Ganitkevitch, Juri, Van Durme, Benjamin and Callison-Burch, Chris (2013). PPDB: The paraphrase database. In Proceedings of NAACL-HLT 2013 Atlanta, Georgia, June 2013, pp. 758–764. www.aclweb.org/anthology/N13-1092.pdf.Google Scholar
Gupta, Rohit and Orăsan, Constantin (2014). Incorporating paraphrasing in translation memory matching and retrieval. In Proceedings of the European Association of Machine Translation (EAMT–2014), pp. 3–10.Google Scholar
Gupta, Rohit, Orăsan, Constantin, Zampieri, Marcos, Vela, Mihaela and Josef van, Genabith (2015). Can translation memories afford not to use paraphrasing? In Proceedings of the 2015 Conference on European Association of Machine Translation (EAMT-2015). Antalya, Turkey, 2015, pp. 35–42.Google Scholar
Hokamp, Chris (2015). Leveraging NLP technologies and linked open data to create better CAT tools. Localisation Focus – The International Journal of Localisation, 14(1), 14–18. www.localisation.ie/resources/publications/2015/258.Google Scholar
Hokamp, Chris and Liu, Qun (2015). HandyCAT: The flexible CAT tool for translation research. In Proceedings of the 18th Annual Conference of the European Association for Machine Translation, Istanbul, Turkey, May, p. 216.Google Scholar
Kenny, Dorothy (2011). Electronic tools and resources for translators. In Malmkjær, Kirsten and Windle, Kevin (eds.), The Oxford Handbook of Translation Studies, pp. 455–472. Oxford, UK: Oxford University Press. www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199239306.001.0001/oxfordhb-9780199239306-e-031.Google Scholar
Li, Liangyou, Way, Andy and Liu, Qun (2015). Dependency graph-to-string translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, pp. 33–43. http://aclweb.org/anthology/D15-1004.CrossRefGoogle Scholar
Li, Liangyou, Parra Escartín, Carla and Liu, Qun (2016). Combining translation memories and syntax-based SMT: Experiments with real industrial data, Baltic Journal of Modern Computing 4(2) (June), 165–177.Google Scholar
Logacheva, Varvara and Specia, Lucia (2015). The role of artificially generated negative data for quality estimation of machine translation. In Proceedings of the 18th Annual Conference of the European Association for Machine Translation, Antalya, Turkey, 2015, pp. 51–58. www.aclweb.org/anthology/W/W15/W15-4907.pdf.Google Scholar
Nayek, Tapas, Naskar, Sudip Kumar, Pal, Santanu, Zampieri, Marcos, Vela, Mihaela and van Genabith, Josef (2015). CATaLog: New approaches to TM and post editing interfaces. In Proceedings of the 1st Workshop on Natural Language Processing for Translation Memories. Workshop on Natural Language Processing for Translation Memories (NLP4TM), located at RANLP 2015, September 11, Hissar, Bulgaria, pp. 36–43. www.aclweb.org/anthology/W15-5206.Google Scholar
Nayek, Tapas, Pal, Santanu, Naskar, Sudip Kumar, Bandyopadhyay, Sivaji and van Genabith, Josef (2016). Beyond translation memories: Generating translation suggestions based on parsing and POS tagging. In Proceedings of the 2nd Workshop on Natural Language Processing for Translation Memories (NLP4TM 2016), Portorož, Slovenia, May 2016.Google Scholar
Neubig, Graham, Nakata, Yosuke and Mori, Shinsuke (2011). Pointwise prediction for robust, adaptable Japanese morphological analysis. In The Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), Portland, Oregon, USA, June 2011, pp. 529–533. www.phontron.com/paper/neubig11aclshort.pdf.Google Scholar
Nguyen, Dat Quoc, Nguyen, Dai Quoc, Pham, Dang Duc and Pham, Son Bao (2014). RDRPOSTagger: A ripple down rules-based part-of-speech tagger. Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, 26–30 April. Association for Computational Linguistics, pp. 17–20.Google Scholar
Orăsan, Constantin, Cattelan, Alessandro, Pastor, Gloria Corpas, van Genabith, Josef, Herranz, Manuel, Arevalillo, Juan José, Liu, Qun, Sima’an, Khalil and Specia, Lucia (2015). The EXPERT project: Advancing the state of the art in hybrid translation technologies. In Proceedings of Translating and the Computer 37, London, UK, 2015. Geneva: Editions Tradulex.Google Scholar
Pal, Santanu, Zampieri, Marcos, Naskar, Sudip Kumar, Nayak, Tapas, Vela, Mihaela and van Genabith, Josef (2016). CATaLog online: Porting a post-editing tool to the web. In Calzolari, Nicoletta (Conference Chair), Choukri, Khalid, Declerck, Thierry, Goggi, Sara, Grobelnik, Marko, Maegaard, Bente, Mariani, Joseph, Mazo, Helene, Moreno, Asuncion, Odijk, Jan and Piperidis, Stelios (eds.), Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France, May 2016. European Language Resources Association (ELRA).Google Scholar
Parra Escartín, Carla, Béchara, Hanna and Orăsan, Constantin (2017). Questing for quality estimation: A user study, The Prague Bulletin of Mathematical Linguistics 108, 343–354. DOI:10.1515/pralin-2017-0032.https://ufal.mff.cuni.cz/pbml/108/art-bechara-escartin-orasan.pdf.Google Scholar
Petrov, Slav, Das, Dipanjan and McDonald, Ryan (2012). A universal part-of-speech tagset. In Calzolari, Nicoletta (Conference Chair), Choukri, Khalid, Declerck, Thierry, Uğur Doğan, Mehmet, Maegaard, Bente, Mariani, Joseph, Moreno, Asuncion, Odijk, Jan and Piperidis, Stelios (eds.), Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12). European Language Resources Association (ELRA). Istanbul, Turkey, May 2012.Google Scholar
Scarton, Carolina and Specia, Lucia (2014). Document-level translation quality estimation: Exploring discourse and pseudo-references. In Proceedings of the Seventeenth Annual Conference of the European Association for Machine Translation (EAMT 2014), Dubrovnik, Croatia, 2014. Zagreb: Hrvatsko društvo za jezične tehnologije, pp. 101–108.Google Scholar
Schmid, Helmut (1994). Probabilistic part-of-speech tagging using decision trees. In Proceedings of the International Conference on New Methods in Language Processing, Manchester, UK.Google Scholar
Tan, Liling and Pal, Santanu (2014). Manawi: Using multi-word expressions and named entities to improve machine translation. In Proceedings of the Ninth Workshop on Statistical Machine Translation, Baltimore, Maryland, USA, 2014, pp. 201–206.CrossRefGoogle Scholar
Trombetti, Marco (2009). Creating the world’s largest translation memory. In MT Summit XII: Proceedings of the Twelfth Machine Translation Summit, Ottawa, ON, Canada, 2009, pp. 9–16.Google Scholar
Varga, Dániel, Halácsy, Péter, Kornai, András, Nagy, Viktor, Ńemeth, László and Trón, Viktor (2005). Parallel corpora for medium density languages. In Recent Advances in Natural Language Processing (RANLP 2005). Bulgaria: INCOMA Ltd., pp. 590–596.Google Scholar
Zaretskaya, Anna, Pastor, Gloria Corpas and Seghiri, Miriam (2015). Translators’ requirements for translation technologies: Results of a user survey. In Proceedings of the AIETI7 Conference: New Horizons in Translation and Interpreting Studies (AIETI), Malaga, Spain, 2015. Geneva: Editions Tradulex, pp. 103–108.Google Scholar
Zaretskaya, Anna, Pastor, Gloria Corpas and Seghiri, Miriam (2018). User perspective on translation tools: Findings of a user survey. In Pastor, Gloria Corpas and Duran, Isabel (eds.), Trends in E-tools and Resources for Translators and Interpreters. The Netherlands: Brill, pp. 37–56.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×