Abstract is missing.
- Competitiveness Analysis of the European Machine Translation MarketAndrejs Vasiljevs, Inguna Skadina, Indra Samite, Kaspars Kaulins, Eriks Ajausks, Julija Melnika, Aivars Berzins. 1-7 [doi]
- Improving CAT Tools in the Translation Workflow: New Approaches and EvaluationMihaela Vela, Santanu Pal, Marcos Zampieri, Sudip Kumar Naskar, Josef van Genabith. 8-15 [doi]
- Hungarian translators' perceptions of Neural Machine Translation in the European CommissionÁgnes Lesznyák. 16-22 [doi]
- Applying Machine Translation to Psychology: Automatic Translation of Personality AdjectivesRitsuko Iwai, Daisuke Kawahara, Takatsune Kumada, Sadao Kurohashi. 23-29 [doi]
- Evaluating machine translation in a low-resource language combination: Spanish-GalicianMaría Do Campo Bayón, Pilar Sánchez-Gijón. 30-35 [doi]
- MTPE in Patents: A Successful Business StoryValeria Premoli, Elena Murgolo, Diego Cresceri. 36-41 [doi]
- User expectations towards machine translation: A case studyBarbara Heinisch, Vesna Lusicky. 42-48 [doi]
- Does NMT make a difference when post-editing closely related languages? The case of Spanish-CatalanSergi Alvarez, Antoni Oliver 0001, Toni Badia. 49-56 [doi]
- Machine Translation in the Financial Services Industry: A Case StudyMara Nunziatini. 57-63 [doi]
- Pre-editing Plus Neural Machine Translation for Subtitling: Effective Pre-editing Rules for Subtitling of TED TalksYusuke Hiraoka, Masaru Yamada. 64-72 [doi]
- Do translator trainees trust machine translation? An experiment on post-editing and revisionRandy Scansani, Silvia Bernardini, Adriano Ferraresi, Luisa Bentivogli. 73-79 [doi]
- On reducing translation shifts in translations intended for MT evaluationMaja Popovic. 80-87 [doi]
- Comparative Analysis of Errors in MT Output and Computer-assisted Translation: Effect of the Human FactorIrina Ovchinnikova, Daria Morozova. 88-94 [doi]
- A Comparative Study of English-Chinese Translations of Court Texts by Machine and Human Translators and the Word2Vec Based Similarity Measure's Ability To Gauge Human Evaluation BiasesMing Qian, Jessie Liu, Chaofeng Li, Liming Pals. 95-100 [doi]
- Translating Terminologies: A Comparative Examination of NMT and PBSMT SystemsLong-Huei Chen, Kyo Kageura. 101-108 [doi]
- NEC TM DATA PROJECTAlexandre Helle, Manuel Herranz. 109 [doi]
- APE-QUESTJoachim Van den Bogaert, Heidi Depraetere, Sara Szoc, Tom Vanallemeersch, Koen Van Winckel, Frederic Everaert, Lucia Specia, Julia Ive, Maxim Khalilov, Christine Maroti, Eduardo Farah, Artur Ventura. 110-111 [doi]
- PRINCIPLE: Providing Resources in Irish, Norwegian, Croatian and Icelandic for the Purposes of Language EngineeringAndy Way, Federico Gaspari. 112-113 [doi]
- iADAATPA Project: Pangeanic use casesMercedes García-Martínez, Amando Estela, Laurent Bié, Alexandre Helle, Manuel Herranz. 114-115 [doi]
- MICEJoachim Van den Bogaert, Heidi Depraetere, Tom Vanallemeersch, Frederic Everaert, Koen Van Winckel, Katri Tammsaar, Ingmar Vali, Tambet Artma, Piret Saartee, Laura Katariina Teder, Arturs Vasilevskis, Valters Sics, Johan Haelterman, David Bienfait. 116-117 [doi]
- ParaCrawl: Web-scale parallel corpora for the languages of the EUMiquel Esplà-Gomis, Mikel L. Forcada, Gema Ramírez-Sánchez, Hieu Hoang. 118-119 [doi]
- Pivot Machine Translation in INTERACT ProjectChao-Hong Liu, Andy Way, Catarina Silva 0003, André Martins. 120-121 [doi]
- Global Under-Resourced Media Translation (GoURMET)Alexandra Birch, Barry Haddow, Ivan Titov, Antonio Valerio Miceli Barone, Rachel Bawden, Felipe Sánchez-Martínez, Mikel L. Forcada, Miquel Esplà-Gomis, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Wilker Aziz, Andrew Secker, Peggy van der Kreeft. 122 [doi]
- Neural machine translation system for the Kazakh languageUalsher Tukeyev, Zhandos Zhumanov. 123-124 [doi]
- Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation ModelsDaniel Torregrosa, Nivranshu Pasricha, Maraim Masoud, Bharathi Raja Chakravarthi, Juan Alonso, Noe Casas, Mihael Arcan. 125-133 [doi]
- Bootstrapping a Natural Language Interface to a Cyber Security Event Collection System using a Hybrid Translation ApproachJohann Roturier, Brian Schlatter, David Silva Schlatter. 134-141 [doi]
- Improving Robustness in Real-World Neural Machine Translation EnginesRohit Gupta, Patrik Lambert, Raj Nath Patel, John Tinsley. 142-148 [doi]
- Surveying the potential of using speech technologies for post-editing purposes in the context of international organizations: What do professional translators think?Jeevanthi Liyanapathirana, Pierrette Bouillon, Bartolomé Mesa-Lao. 149-158 [doi]
- Automatic Translation for Software with Safe VelocityDag Schmidtke, Declan Groves. 159-166 [doi]
- Application of Post-Edited Machine Translation in Fashion eCommerceKasia Kosmaczewska, Matt Train. 167-173 [doi]
- Morphological Neural Pre- and Post-Processing for Slavic LanguagesGiorgio Bernardinello. 174-178 [doi]
- Large-scale Machine Translation Evaluation of the iADAATPA ProjectSheila Castilho, Natália Resende, Federico Gaspari, Andy Way, Tony O'Dowd, Marek Mazur, Manuel Herranz, Alexandre Helle, Gema Ramírez-Sánchez, Víctor M. Sánchez-Cartagena, Marcis Pinnis, Valters Sics. 179-185 [doi]
- Collecting domain specific data for MT: an evaluation of the ParaCrawlpipelineArne Defauw, Tom Vanallemeersch, Sara Szoc, Frederic Everaert, Koen Van Winckel, Kim Scholte, Joris Brabers, Joachim Van den Bogaert. 186-195 [doi]
- Monolingual backtranslation in a medical speech translation system for diagnostic interviews - a NMT approachJonathan Mutal, Pierrette Bouillon, Johanna Gerlach, Paula Estrella, Hervé Spechbach. 196-203 [doi]
- Improving Domain Adaptation for Machine Translation withTranslation PiecesCatarina Silva. 204-212 [doi]
- Raising the TM Threshold in Neural MT Post-Editing: a Case Study onTwo DatasetsAnna Zaretskaya. 213-218 [doi]
- Incremental Adaptation of NMT for Professional Post-editors: A User StudyMiguel Domingo, Mercedes García-Martínez, Álvaro Peris, Alexandre Helle, Amando Estela, Laurent Bié, Francisco Casacuberta, Manuel Herranz. 219-227 [doi]
- When less is more in Neural Quality Estimation of Machine Translation. An industry case studyDimitar Shterionov, Félix do Carmo, Joss Moorkens, Eric Paquin, Dag Schmidtke, Declan Groves, Andy Way. 228-235 [doi]