---
title: "EAMT 2020 — all accepted papers with summaries and citations"
description: "All 69 papers of EAMT 2020, the most cited first; those with our summary link to it. Among them: A User Study of the Incremental Learning in…"
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# EAMT 2020: all accepted papers

69 papers · the most cited first · [← EAMT 2020](https://md.research.elamur.ai/free/en/venues/eamt/2020/)

1. **[A User Study of the Incremental Learning in NMT ↗](https://aclanthology.org/2020.eamt-1.34/)**Miguel Domingo, Mercedes García-Martínez, Álvaro Peris et al.
2. **[A human evaluation of English-Irish statistical and neural machine translation ↗](https://aclanthology.org/2020.eamt-1.46/)**Meghan Dowling, Sheila Castilho, Joss Moorkens et al.
3. **[A multi-source approach for Breton–French hybrid machine translation ↗](https://aclanthology.org/2020.eamt-1.8/)**Víctor M. Sánchez-Cartagena, Mikel L. Forcada, Felipe Sánchez-Martínez
4. **[APE-QUEST: an MT Quality Gate ↗](https://aclanthology.org/2020.eamt-1.58/)**Heidi 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 domain ↗](https://aclanthology.org/2020.eamt-1.32/)**Felipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz et al.
6. **[An Overview of the SEBAMAT Project ↗](https://aclanthology.org/2020.eamt-1.66/)**Reinhard Rapp, George Tambouratzis
7. **[Assessing the Comprehensibility of Automatic Translations (ArisToCAT) ↗](https://aclanthology.org/2020.eamt-1.64/)**Lieve Macken, Margot Fonteyne, Arda Tezcan et al.
8. **[Automatic Translation for Multiple NLP tasks: a Multi-task Approach to Machine-oriented NMT Adaptation ↗](https://aclanthology.org/2020.eamt-1.25/)**Amirhossein Tebbifakhr, Matteo Negri, Marco Turchi
9. **[Bifixer and Bicleaner: two open-source tools to clean your parallel data](https://md.research.elamur.ai/free/en/papers/sanchez-2020-bifixer-bicleaner-two-open-source/)**Gema Ramírez-Sánchez, Jaume Zaragoza-Bernabeu, Marta Bañón et al.*our summary*
10. **[CEF Data Marketplace: Powering a Long-term Supply of Language Data ↗](https://aclanthology.org/2020.eamt-1.51/)**Amir Kamran, Dace Dzeguze, Jaap van der Meer et al.
11. **[CEFAT4Cities, a Natural Language Layer for the ISA2 Core Public Service Vocabulary ↗](https://aclanthology.org/2020.eamt-1.63/)**Joachim Van den Bogaert, Arne Defauw, Sara Szoc et al.
12. **[Comparing Post-editing based on Four Editing Actions against Translating with an Auto-Complete Feature ↗](https://aclanthology.org/2020.eamt-1.45/)**Félix Do Carmo
13. **[Comprehension and Trust in Crises: Investigating the Impact of Machine Translation and Post-Editing ↗](https://aclanthology.org/2020.eamt-1.2/)**Alessandra Rossetti, Sharon O'Brien, Patrick Cadwell
14. **[Correct Me If You Can: Learning from Error Corrections and Markings ↗](https://aclanthology.org/2020.eamt-1.15/)**Julia Kreutzer, Nathaniel Berger, Stefan Riezler
15. **[DeepSPIN: Deep Structured Prediction for Natural Language Processing ↗](https://aclanthology.org/2020.eamt-1.67/)**André F. T. Martins
16. **[Document-Level Machine Translation Evaluation Project: Methodology, Effort and Inter-Annotator Agreement ↗](https://aclanthology.org/2020.eamt-1.49/)**Sheila Castilho
17. **[Document-level Neural MT: A Systematic Comparison](https://md.research.elamur.ai/free/en/papers/lopes-2020-document-level-neural-mt-systematic/)**António Lopes, M. Amin Farajian, Rachel Bawden et al.*our summary*
18. **[Domain Informed Neural Machine Translation: Developing Translation Services for Healthcare Enterprise ↗](https://aclanthology.org/2020.eamt-1.27/)**Sahil Manchanda, Galina Grunin
19. **[Double Attention-based Multimodal Neural Machine Translation with Semantic Image Regions ↗](https://aclanthology.org/2020.eamt-1.12/)**Yuting Zhao, Mamoru Komachi, Tomoyuki Kajiwara et al.
20. **[ELITR: European Live Translator ↗](https://aclanthology.org/2020.eamt-1.53/)**Ondřej Bojar, Dominik Macháček, Sangeet Sagar et al.
21. **[Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer ↗](https://aclanthology.org/2020.eamt-1.4/)**Hao Yang, Minghan Wang, Ning Xie et al.
22. **[Efficiently Reusing Old Models Across Languages via Transfer Learning ↗](https://aclanthology.org/2020.eamt-1.3/)**Tom Kocmi, Ondřej Bojar
23. **[Ellipsis Translation for a Medical Speech to Speech Translation System ↗](https://aclanthology.org/2020.eamt-1.30/)**Jonathan Mutal, Johanna Gerlach, Pierrette Bouillon et al.
24. **[Estimation vs Metrics: is QE Useful for MT Model Selection? ↗](https://aclanthology.org/2020.eamt-1.36/)**Anna Zaretskaya, José Conceição, Frederick Bane
25. **[Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission ↗](https://aclanthology.org/2020.eamt-1.28/)**Karolina Stefaniak
26. **[Fine-Grained Error Analysis on English-to-Japanese Machine Translation in the Medical Domain ↗](https://aclanthology.org/2020.eamt-1.17/)**Takeshi Hayakawa, Yuki Arase
27. **[Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-Chinese ↗](https://aclanthology.org/2020.eamt-1.14/)**Yuying Ye, Antonio Toral
28. **[How do LSPs compute MT discounts? Presenting a company's pipeline and its use ↗](https://aclanthology.org/2020.eamt-1.42/)**Randy Scansani, Lamis Mhedhbi
29. **[INMIGRA3: building a case for NGOs and NMT ↗](https://aclanthology.org/2020.eamt-1.56/)**Celia Rico, María Del Mar Sánchez Ramos, Antoni Oliver
30. **[Incorporating External Annotation to improve Named Entity Translation in NMT ↗](https://aclanthology.org/2020.eamt-1.6/)**Maciej Modrzejewski, Miriam Exel, Bianka Buschbeck et al.
31. **[Insights from Gathering MT Productivity Metrics at Scale ↗](https://aclanthology.org/2020.eamt-1.38/)**Georg Kirchner
32. **[Intelligent Translation Memory Matching and Retrieval with Sentence Encoders ↗](https://aclanthology.org/2020.eamt-1.19/)**Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov
33. **[Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings ↗](https://aclanthology.org/2020.eamt-1.22/)**António Góis, Kyunghyun Cho, André Martins
34. **[Let MT simplify and speed up your Alignment for TM creation ↗](https://aclanthology.org/2020.eamt-1.65/)**Judith Klein, Giorgio Bernardinello
35. **[Leveraging Multilingual Resources for Language Invariant Sentiment Analysis ↗](https://aclanthology.org/2020.eamt-1.9/)**Allen Antony, Arghya Bhattacharya, Jaipal Goud et al.
36. **[Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution ↗](https://aclanthology.org/2020.eamt-1.10/)**Lukas Edman, Antonio Toral, Gertjan van Noord
37. **[MICE: a middleware layer for MT ↗](https://aclanthology.org/2020.eamt-1.59/)**Joachim Van den Bogaert, Tom Vanallemeersch, Heidi Depraetere
38. **[MT for subtitling: User evaluation of post-editing productivity ↗](https://aclanthology.org/2020.eamt-1.13/)**Maarit Koponen, Umut Sulubacak, Kaisa Vitikainen et al.
39. **[MT syntactic priming effects on L2 English speakers](https://md.research.elamur.ai/free/en/papers/resende-2020-mt-syntactic-priming-effects-l2/)**Natália Resende, Benjamin Cowan, Andy Way*our summary*
40. **[MTUOC: easy and free integration of NMT systems in professional translation environments ↗](https://aclanthology.org/2020.eamt-1.55/)**Antoni Oliver
41. **[MTrill project: Machine Translation impact on language learning ↗](https://aclanthology.org/2020.eamt-1.69/)**Natália Resende, Andy Way
42. **[Machine Translation Post-Editing Levels: Breaking Away from the Tradition and Delivering a Tailored Service ↗](https://aclanthology.org/2020.eamt-1.33/)**Mara Nunziatini, Lena Marg
43. **[Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputs ↗](https://aclanthology.org/2020.eamt-1.47/)**Maria Stasimioti, Vilelmini Sosoni, Katia Kermanidis et al.
44. **[Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine Translation ↗](https://aclanthology.org/2020.eamt-1.21/)**Kamal Kumar Gupta, Rejwanul Haque, Asif Ekbal et al.
45. **[Multidimensional assessment of the eTranslation output for English–Slovene ↗](https://aclanthology.org/2020.eamt-1.41/)**Mateja Arnejšek, Alenka Unk
46. **[NICE: Neural Integrated Custom Engines ↗](https://aclanthology.org/2020.eamt-1.35/)**Daniel Marín Buj, Daniel Ibáñez García, Zuzanna Parcheta et al.
47. **[Neural Translation for the European Union (NTEU) Project ↗](https://aclanthology.org/2020.eamt-1.60/)**Laurent Bié, Aleix Cerdà-i-Cucó, Hans Degroote et al.
48. **[OCR, Classification & Machine Translation (OCCAM) ↗](https://aclanthology.org/2020.eamt-1.62/)**Joachim Van den Bogaert, Arne Defauw, Frederic Everaert et al.
49. **[OPUS-MT – Building open translation services for the World ↗](https://aclanthology.org/2020.eamt-1.61/)**Jörg Tiedemann, Santhosh Thottingal
50. **[On the differences between human translations ↗](https://aclanthology.org/2020.eamt-1.39/)**Maja Popovic
51. **[Persistent MT on software technical documentation - a case study ↗](https://aclanthology.org/2020.eamt-1.37/)**María Concepción Laguardia
52. **[PosEdiOn: Post-Editing Assessment in PythOn ↗](https://aclanthology.org/2020.eamt-1.43/)**Antoni Oliver, Sergi Alvarez, Toni Badia
53. **[Progress of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian ↗](https://aclanthology.org/2020.eamt-1.54/)**Andy Way, Petra Bago, Jane Dunne et al.
54. **[Project MAIA: Multilingual AI Agent Assistant ↗](https://aclanthology.org/2020.eamt-1.68/)**André F. T. Martins, Joao Graca, Paulo Dimas et al.
55. **[QE Viewer: an Open-Source Tool for Visualization of Machine Translation Quality Estimation Results ↗](https://aclanthology.org/2020.eamt-1.48/)**Felipe Soares, Anna Zaretskaya, Diego Bartolome
56. **[QRev: Machine Translation of User Reviews: What Influences the Translation Quality? ↗](https://aclanthology.org/2020.eamt-1.52/)**Maja Popovic
57. **[Quality In, Quality Out: Learning from Actual Mistakes ↗](https://aclanthology.org/2020.eamt-1.16/)**Frederic Blain, Nikolaos Aletras, Lucia Specia
58. **[Quantitative Analysis of Post-Editing Effort Indicators for NMT ↗](https://aclanthology.org/2020.eamt-1.44/)**Sergi Alvarez, Antoni Oliver, Toni Badia
59. **[Re-design of the Machine Translation Training Tool (MT3) ↗](https://aclanthology.org/2020.eamt-1.40/)**Paula Estrella, Emiliano Cuenca, Laura Bruno et al.
60. **[Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019 ↗](https://aclanthology.org/2020.eamt-1.20/)**Antonio Toral
61. **[Revisiting Round-trip Translation for Quality Estimation ↗](https://aclanthology.org/2020.eamt-1.11/)**Jihyung Moon, Hyunchang Cho, Eunjeong L. Park
62. **[Sockeye 2: A Toolkit for Neural Machine Translation ↗](https://aclanthology.org/2020.eamt-1.50/)**Felix Hieber, Tobias Domhan, Michael Denkowski et al.
63. **[Terminology-Constrained Neural Machine Translation at SAP ↗](https://aclanthology.org/2020.eamt-1.29/)**Miriam Exel, Bianka Buschbeck, Lauritz Brandt et al.
64. **[The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) Project ↗](https://aclanthology.org/2020.eamt-1.57/)**Ēriks Ajausks, Victoria Arranz, Laurent Bié et al.
65. **[The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction ↗](https://aclanthology.org/2020.eamt-1.1/)**Felix Stahlberg
66. **[Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning ↗](https://aclanthology.org/2020.eamt-1.7/)**Minghan 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 MT ↗](https://aclanthology.org/2020.eamt-1.23/)**Lukas Fischer, Samuel Läubli
68. **[When and Why is Unsupervised Neural Machine Translation Useless? ↗](https://aclanthology.org/2020.eamt-1.5/)**Yunsu Kim, Miguel Graça, Hermann Ney
69. **[With or without you? Effects of using machine translation to write flash fiction in the foreign language ↗](https://aclanthology.org/2020.eamt-1.18/)**Nora Aranberri
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