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EAMT 2020: all accepted papers

69 papers · the most cited first · ← EAMT 2020

  1. A User Study of the Incremental Learning in NMTMiguel Domingo, Mercedes García-Martínez, Álvaro Peris et al.
  2. A human evaluation of English-Irish statistical and neural machine translationMeghan Dowling, Sheila Castilho, Joss Moorkens et al.
  3. A multi-source approach for Breton–French hybrid machine translationVíctor M. Sánchez-Cartagena, Mikel L. Forcada, Felipe Sánchez-Martínez
  4. APE-QUEST: an MT Quality GateHeidi Depraetere, Joachim Van den Bogaert, Sara Szoc et al.
  5. 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 et al.
  6. An Overview of the SEBAMAT ProjectReinhard Rapp, George Tambouratzis
  7. Assessing the Comprehensibility of Automatic Translations (ArisToCAT)Lieve Macken, Margot Fonteyne, Arda Tezcan et al.
  8. Automatic Translation for Multiple NLP tasks: a Multi-task Approach to Machine-oriented NMT AdaptationAmirhossein Tebbifakhr, Matteo Negri, Marco Turchi
  9. Bifixer and Bicleaner: two open-source tools to clean your parallel dataGema Ramírez-Sánchez, Jaume Zaragoza-Bernabeu, Marta Bañón et al.our summary
  10. CEF Data Marketplace: Powering a Long-term Supply of Language DataAmir Kamran, Dace Dzeguze, Jaap van der Meer et al.
  11. CEFAT4Cities, a Natural Language Layer for the ISA2 Core Public Service VocabularyJoachim Van den Bogaert, Arne Defauw, Sara Szoc et al.
  12. Comparing Post-editing based on Four Editing Actions against Translating with an Auto-Complete FeatureFélix Do Carmo
  13. Comprehension and Trust in Crises: Investigating the Impact of Machine Translation and Post-EditingAlessandra Rossetti, Sharon O'Brien, Patrick Cadwell
  14. Correct Me If You Can: Learning from Error Corrections and MarkingsJulia Kreutzer, Nathaniel Berger, Stefan Riezler
  15. DeepSPIN: Deep Structured Prediction for Natural Language ProcessingAndré F. T. Martins
  16. Document-Level Machine Translation Evaluation Project: Methodology, Effort and Inter-Annotator AgreementSheila Castilho
  17. Document-level Neural MT: A Systematic ComparisonAntónio Lopes, M. Amin Farajian, Rachel Bawden et al.our summary
  18. Domain Informed Neural Machine Translation: Developing Translation Services for Healthcare EnterpriseSahil Manchanda, Galina Grunin
  19. Double Attention-based Multimodal Neural Machine Translation with Semantic Image RegionsYuting Zhao, Mamoru Komachi, Tomoyuki Kajiwara et al.
  20. ELITR: European Live TranslatorOndřej Bojar, Dominik Macháček, Sangeet Sagar et al.
  21. Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter LayerHao Yang, Minghan Wang, Ning Xie et al.
  22. Efficiently Reusing Old Models Across Languages via Transfer LearningTom Kocmi, Ondřej Bojar
  23. Ellipsis Translation for a Medical Speech to Speech Translation SystemJonathan Mutal, Johanna Gerlach, Pierrette Bouillon et al.
  24. Estimation vs Metrics: is QE Useful for MT Model Selection?Anna Zaretskaya, José Conceição, Frederick Bane
  25. Evaluating the usefulness of neural machine translation for the Polish translators in the European CommissionKarolina Stefaniak
  26. Fine-Grained Error Analysis on English-to-Japanese Machine Translation in the Medical DomainTakeshi Hayakawa, Yuki Arase
  27. Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-ChineseYuying Ye, Antonio Toral
  28. How do LSPs compute MT discounts? Presenting a company's pipeline and its useRandy Scansani, Lamis Mhedhbi
  29. INMIGRA3: building a case for NGOs and NMTCelia Rico, María Del Mar Sánchez Ramos, Antoni Oliver
  30. Incorporating External Annotation to improve Named Entity Translation in NMTMaciej Modrzejewski, Miriam Exel, Bianka Buschbeck et al.
  31. Insights from Gathering MT Productivity Metrics at ScaleGeorg Kirchner
  32. Intelligent Translation Memory Matching and Retrieval with Sentence EncodersTharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov
  33. Learning Non-Monotonic Automatic Post-Editing of Translations from Human OrderingsAntónio Góis, Kyunghyun Cho, André Martins
  34. Let MT simplify and speed up your Alignment for TM creationJudith Klein, Giorgio Bernardinello
  35. Leveraging Multilingual Resources for Language Invariant Sentiment AnalysisAllen Antony, Arghya Bhattacharya, Jaipal Goud et al.
  36. Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated SolutionLukas Edman, Antonio Toral, Gertjan van Noord
  37. MICE: a middleware layer for MTJoachim Van den Bogaert, Tom Vanallemeersch, Heidi Depraetere
  38. MT for subtitling: User evaluation of post-editing productivityMaarit Koponen, Umut Sulubacak, Kaisa Vitikainen et al.
  39. MT syntactic priming effects on L2 English speakersNatália Resende, Benjamin Cowan, Andy Wayour summary
  40. MTUOC: easy and free integration of NMT systems in professional translation environmentsAntoni Oliver
  41. MTrill project: Machine Translation impact on language learningNatália Resende, Andy Way
  42. Machine Translation Post-Editing Levels: Breaking Away from the Tradition and Delivering a Tailored ServiceMara Nunziatini, Lena Marg
  43. Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputsMaria Stasimioti, Vilelmini Sosoni, Katia Kermanidis et al.
  44. Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine TranslationKamal Kumar Gupta, Rejwanul Haque, Asif Ekbal et al.
  45. Multidimensional assessment of the eTranslation output for English–SloveneMateja Arnejšek, Alenka Unk
  46. NICE: Neural Integrated Custom EnginesDaniel Marín Buj, Daniel Ibáñez García, Zuzanna Parcheta et al.
  47. Neural Translation for the European Union (NTEU) ProjectLaurent Bié, Aleix Cerdà-i-Cucó, Hans Degroote et al.
  48. OCR, Classification & Machine Translation (OCCAM)Joachim Van den Bogaert, Arne Defauw, Frederic Everaert et al.
  49. OPUS-MT – Building open translation services for the WorldJörg Tiedemann, Santhosh Thottingal
  50. On the differences between human translationsMaja Popovic
  51. Persistent MT on software technical documentation - a case studyMaría Concepción Laguardia
  52. PosEdiOn: Post-Editing Assessment in PythOnAntoni Oliver, Sergi Alvarez, Toni Badia
  53. Progress of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and NorwegianAndy Way, Petra Bago, Jane Dunne et al.
  54. Project MAIA: Multilingual AI Agent AssistantAndré F. T. Martins, Joao Graca, Paulo Dimas et al.
  55. QE Viewer: an Open-Source Tool for Visualization of Machine Translation Quality Estimation ResultsFelipe Soares, Anna Zaretskaya, Diego Bartolome
  56. QRev: Machine Translation of User Reviews: What Influences the Translation Quality?Maja Popovic
  57. Quality In, Quality Out: Learning from Actual MistakesFrederic Blain, Nikolaos Aletras, Lucia Specia
  58. Quantitative Analysis of Post-Editing Effort Indicators for NMTSergi Alvarez, Antoni Oliver, Toni Badia
  59. Re-design of the Machine Translation Training Tool (MT3)Paula Estrella, Emiliano Cuenca, Laura Bruno et al.
  60. Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019Antonio Toral
  61. Revisiting Round-trip Translation for Quality EstimationJihyung Moon, Hyunchang Cho, Eunjeong L. Park
  62. Sockeye 2: A Toolkit for Neural Machine TranslationFelix Hieber, Tobias Domhan, Michael Denkowski et al.
  63. Terminology-Constrained Neural Machine Translation at SAPMiriam Exel, Bianka Buschbeck, Lauritz Brandt et al.
  64. The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) ProjectĒriks Ajausks, Victoria Arranz, Laurent Bié et al.
  65. The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence PredictionFelix Stahlberg
  66. Unified Humor Detection Based on Sentence-pair Augmentation and Transfer LearningMinghan Wang, Hao Yang, Ying Qin et al.
  67. What's the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MTLukas Fischer, Samuel Läubli
  68. When and Why is Unsupervised Neural Machine Translation Useless?Yunsu Kim, Miguel Graça, Hermann Ney
  69. With or without you? Effects of using machine translation to write flash fiction in the foreign languageNora Aranberri

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