Table of Contents

• The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence PredictionFelix Stahlberg. 5-6 [doi]
• Comprehension and Trust in Crises: Investigating the Impact of Machine Translation and Post-EditingAlessandra Rossetti, Sharon O'Brien, Patrick Cadwell. 9-18 [doi]
• Efficiently Reusing Old Models Across Languages via Transfer LearningTom Kocmi, Ondrej Bojar. 19-28 [doi]
• Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter LayerHao Yang, Minghan Wang, Ning Xie, Ying Qin, Yao Deng. 29-34 [doi]
• When and Why is Unsupervised Neural Machine Translation Useless?Yunsu Kim, Miguel Graça, Hermann Ney. 35-44 [doi]
• Incorporating External Annotation to improve Named Entity Translation in NMTMaciej Modrzejewski, Miriam Exel, Bianka Buschbeck, Thanh-Le Ha, Alexander H. Waibel. 45-51 [doi]
• Unified Humor Detection Based on Sentence-pair Augmentation and Transfer LearningMinghan Wang, Hao Yang, Ying Qin, Shiliang Sun, Yao Deng. 53-59 [doi]
• A multi-source approach for Breton-French hybrid machine translationVíctor M. Sánchez-Cartagena, Mikel L. Forcada, Felipe Sánchez-Martínez. 61-70 [doi]
• Leveraging Multilingual Resources for Language Invariant Sentiment AnalysisAllen Antony, Arghya Bhattacharya, Jaipal Goud, Radhika Mamidi. 71-79 [doi]
• Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated SolutionLukas Edman, Antonio Toral, Gertjan van Noord. 81-90 [doi]
• Revisiting Round-trip Translation for Quality EstimationJihyung Moon, Hyunchang Cho, Eunjeong L. Park. 91-104 [doi]
• Double Attention-based Multimodal Neural Machine Translation with Semantic Image RegionsYuting Zhao, Mamoru Komachi, Tomoyuki Kajiwara, Chenhui Chu. 105-114 [doi]
• MT for subtitling: User evaluation of post-editing productivityMaarit Koponen, Umut Sulubacak, Kaisa Vitikainen, Jörg Tiedemann. 115-124 [doi]
• Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-ChineseYuying Ye, Antonio Toral. 125-134 [doi]
• Correct Me If You Can: Learning from Error Corrections and MarkingsJulia Kreutzer, Nathaniel Berger, Stefan Riezler. 135-144 [doi]
• Quality In, Quality Out: Learning from Actual MistakesFrédéric Blain, Nikolaos Aletras, Lucia Specia. 145-153 [doi]
• Fine-Grained Error Analysis on English-to-Japanese Machine Translation in the Medical DomainTakeshi Hayakawa, Yuki Arase. 155-164 [doi]
• With or without you? Effects of using machine translation to write flash fiction in the foreign languageNora Aranberri. 165-174 [doi]
• Intelligent Translation Memory Matching and Retrieval with Sentence EncodersTharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov. 175-184 [doi]
• Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019Antonio Toral. 185-194 [doi]
• Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine TranslationKamal Kumar Gupta, Rejwanul Haque, Asif Ekbal, Pushpak Bhattacharyya, Andy Way. 195-204 [doi]
• Learning Non-Monotonic Automatic Post-Editing of Translations from Human OrderingsAntónio Góis, KyungHyun Cho, André F. T. Martins. 205-214 [doi]
• What's the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MTLukas Fischer, Samuel Läubli. 215-224 [doi]
• Document-level Neural MT: A Systematic ComparisonAntónio V. Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, André F. T. Martins. 225-234 [doi]
• Automatic Translation for Multiple NLP tasks: a Multi-task Approach to Machine-oriented NMT AdaptationAmirhossein Tebbifakhr, Matteo Negri, Marco Turchi. 235-244 [doi]
• MT syntactic priming effects on L2 English speakersNatália Resende, Benjamin R. Cowan, Andy Way. 245-253 [doi]
• Domain Informed Neural Machine Translation: Developing Translation Services for Healthcare EnterpriseSahil Manchanda, Galina Grunin. 255-261 [doi]
• Evaluating the usefulness of neural machine translation for the Polish translators in the European CommissionKarolina Stefaniak. 263-269 [doi]
• Terminology-Constrained Neural Machine Translation at SAPMiriam Exel, Bianka Buschbeck, Lauritz Brandt, Simona Doneva. 271-280 [doi]
• Ellipsis Translation for a Medical Speech to Speech Translation SystemJonathan Mutal, Johanna Gerlach, Pierrette Bouillon, Hervé Spechbach. 281-290 [doi]
• Bifixer and Bicleaner: two open-source tools to clean your parallel dataGema Ramírez-Sánchez, Jaume Zaragoza-Bernabeu, Marta Bañón, Sergio Ortiz-Rojas. 291-298 [doi]
• An English-Swahili parallel corpus and its use for neural machine translation in the news domainFelipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Mikel L. Forcada, Miquel Esplà-Gomis, Andrew Secker, Susie Coleman, Julie Wall. 299-308 [doi]
• Machine Translation Post-Editing Levels: Breaking Away from the Tradition and Delivering a Tailored ServiceMara Nunziatini, Lena Marg. 309-318 [doi]
• A User Study of the Incremental Learning in NMTMiguel Domingo, Mercedes García-Martínez, Álvaro Peris, Alexandre Helle, Amando Estela, Laurent Bié, Francisco Casacuberta, Manuel Herranz. 319-328 [doi]
• NICE: Neural Integrated Custom EnginesDaniel Marín Buj, Daniel Ibáñez García, Zuzanna Parcheta, Francisco Casacuberta. 329-338 [doi]
• Estimation vs Metrics: is QE Useful for MT Model Selection?Anna Zaretskaya, José Conceição, Frederick Bane. 339-346 [doi]
• Persistent MT on software technical documentation - a case studyMaría Concepción Laguardia. 347-352 [doi]
• Insights from Gathering MT Productivity Metrics at ScaleGeorg Kirchner. 353-362 [doi]
• On the differences between human translationsMaja Popovic. 365-374 [doi]
• Re-design of the Machine Translation Training Tool (MT3)Paula Estrella, Emiliano Cuenca, Laura Bruno, Jonathan Mutal, Sabrina Girletti, Lise Volkart, Pierrette Bouillon. 375-382 [doi]
• Multidimensional assessment of the eTranslation output for English-SloveneMateja Arnejsek, Alenka Unk. 383-392 [doi]
• How do LSPs compute MT discounts? Presenting a company's pipeline and its useRandy Scansani, Lamis Mhedhbi. 393-401 [doi]
• PosEdiOn: Post-Editing Assessment in PythOnAntoni Oliver 0001, Sergi Alvarez, Toni Badia. 403-410 [doi]
• Quantitative Analysis of Post-Editing Effort Indicators for NMTSergi Alvarez, Antoni Oliver 0001, Toni Badia. 411-420 [doi]
• Comparing Post-editing based on Four Editing Actions against Translating with an Auto-Complete FeatureFélix do Carmo. 421-430 [doi]
• A human evaluation of English-Irish statistical and neural machine translationMeghan Dowling, Sheila Castilho, Joss Moorkens, Teresa Lynn, Andy Way. 431-440 [doi]
• Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputsMaria Stasimioti, Vilelmini Sosoni, Katia Kermanidis, Despoina Mouratidis. 441-450 [doi]
• QE Viewer: an Open-Source Tool for Visualization of Machine Translation Quality Estimation ResultsFelipe Soares, Anna Zaretskaya, Diego Bartolomé. 453-454 [doi]
• Document-Level Machine Translation Evaluation Project: Methodology, Effort and Inter-Annotator AgreementSheila Castilho. 455-456 [doi]
• Sockeye 2: A Toolkit for Neural Machine TranslationFelix Hieber, Tobias Domhan, Michael Denkowski, David Vilar. 457-458 [doi]
• CEF Data Marketplace: Powering a Long-term Supply of Language DataAmir Kamran, Dace Dzeguze, Jaap van der Meer, Milica Panic, Alessandro Cattelan, Daniele Patrioli, Luisa Bentivogli, Marco Turchi. 459-460 [doi]
• QRev: Machine Translation of User Reviews: What Influences the Translation Quality?Maja Popovic. 461-462 [doi]
• ELITR: European Live TranslatorOndrej Bojar, Dominik Machácek, Sangeet Sagar, Otakar Smrz, Jonás Kratochvíl, Ebrahim Ansari, Dario Franceschini, Chiara Canton, Ivan Simonini, Thai Son Nguyen, Felix Schneider, Sebastian Stüker, Alex Waibel, Barry Haddow, Rico Sennrich, Philip Williams. 463-464 [doi]
• Progress of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and NorwegianAndy Way, Petra Bago, Jane Dunne, Federico Gaspari, Andre K, Gauti Kristmannsson, Helen McHugh, Jon Arild Olsen, Dana Davis Sheridan, Páraic Sheridan, John Tinsley. 465-466 [doi]
• MTUOC: easy and free integration of NMT systems in professional translation environmentsAntoni Oliver 0001. 467-468 [doi]
• INMIGRA3: building a case for NGOs and NMTCelia Rico, María Del Mar Sánchez Ramos, Antoni Oliver 0001. 469-470 [doi]
• The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) ProjectEriks Ajausks, Victoria Arranz, Laurent Bié, Aleix Cerdà-i-Cucó, Khalid Choukri, Montse Cuadros, Hans Degroote, Amando Estela, Thierry Etchegoyhen, Mercedes García-Martínez, Aitor García Pablos, Manuel Herranz, Alejandro Kohan, Maite Melero, Mike Rosner, Roberts Rozis, Patrick Paroubek, Arturs Vasilevskis, Pierre Zweigenbaum. 471-472 [doi]
• APE-QUEST: an MT Quality GateHeidi Depraetere, Joachim Van den Bogaert, Sara Szoc, Tom Vanallemeersch. 473-474 [doi]
• MICE: a middleware layer for MTJoachim Van den Bogaert, Tom Vanallemeersch, Heidi Depraetere. 475-476 [doi]
• Neural Translation for the European Union (NTEU) ProjectLaurent Bié, Aleix Cerdà-i-Cucó, Hans Degroote, Amando Estela, Mercedes García-Martínez, Manuel Herranz, Alejandro Kohan, Maite Melero, Tony O'Dowd, Sinéad O'Gorman, Marcis Pinnis, Roberts Rozis, Riccardo Superbo, Arturs Vasilevskis. 477-478 [doi]
• OPUS-MT - Building open translation services for the WorldJörg Tiedemann, Santhosh Thottingal. 479-480 [doi]
• OCR, Classification& Machine Translation (OCCAM)Joachim Van den Bogaert, Arne Defauw, Frederic Everaert, Koen Van Winckel, Alina Kramchaninova, Anna Bardadym, Tom Vanallemeersch, Pavel Smrz, Michal Hradis. 481-482 [doi]
• CEFAT4Cities, a Natural Language Layer for the ISA2 Core Public Service VocabularyJoachim Van den Bogaert, Arne Defauw, Sara Szoc, Frederic Everaert, Koen Van Winckel, Alina Kramchaninova, Anna Bardadym, Tom Vanallemeersch. 483-484 [doi]
• Assessing the Comprehensibility of Automatic Translations (ArisToCAT)Lieve Macken, Margot Fonteyne, Arda Tezcan, Joke Daems. 485-486 [doi]
• Let MT simplify and speed up your Alignment for TM creationJudith Klein, Giorgio Bernardinello. 487-489 [doi]
• An Overview of the SEBAMAT ProjectReinhard Rapp, George Tambouratzis. 491-492 [doi]
• DeepSPIN: Deep Structured Prediction for Natural Language ProcessingAndré Filipe Torres Martins. 493-494 [doi]
• Project MAIA: Multilingual AI Agent AssistantAndré Filipe Torres Martins, João Graça, Paulo Dimas, Helena Moniz, Graham Neubig. 495-496 [doi]
• MTrill project: Machine Translation impact on language learningNatália Resende, Andy Way. 497-498 [doi]