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# Works of Annual Conference of the European Association for Machine Translation (EAMT)

54 works · the most cited first · [← Annual Conference of the European Association for…](https://md.research.elamur.ai/free/en/venues/eamt/)

1. [**WIT3: Web Inventory of Transcribed and Translated Talks**Mauro Cettolo, Christian Girardi, Marcello Federico*2012 · 706 citations*](https://md.research.elamur.ai/free/en/papers/cettolo-2012-wit3-web-inventory-transcribed-translated/)
2. [**Estimating the Sentence-Level Quality of Machine Translation Systems**Lucia Specia, Marco Turchi, Nicola Cancedda, et al.*2009 · 351 citations*](https://md.research.elamur.ai/free/en/papers/specia-2009-estimating-sentence-level-quality-machine/)
3. [**Adaptation of the translation model for statistical machine translation based on information retrieval**Almut Silja Hildebrand, Matthias Eck, Stephan Vogel, et al.*2005 · 230 citations*](https://md.research.elamur.ai/free/en/papers/hildebrand-2005-adaptation-translation-model-statistical-machine/)
4. [**Investigating Backtranslation in Neural Machine Translation**Alberto Poncelas, Dimitar Shterionov, Andy Way, et al.*2018 · 184 citations*](https://md.research.elamur.ai/free/en/papers/poncelas-2018-investigating-backtranslation-neural-machine-translation/)
5. [**Document-level Neural MT: A Systematic Comparison**António Lopes, M. Amin Farajian, Rachel Bawden, et al.*2020 · 114 citations*](https://md.research.elamur.ai/free/en/papers/lopes-2020-document-level-neural-mt-systematic/)
6. [**Iterative Translation Refinement with Large Language Models**Pinzhen Chen, Zhicheng Guo, Barry Haddow, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-iterative-translation-refinement-large-language/)
7. [**Bifixer and Bicleaner: two open-source tools to clean your parallel data**Gema Ramírez-Sánchez, Jaume Zaragoza-Bernabeu, Marta Bañón, et al.*2020 · 55 citations*](https://md.research.elamur.ai/free/en/papers/sanchez-2020-bifixer-bicleaner-two-open-source/)
8. [**From Human to Automatic Error Classification for Machine Translation Output**Maja Popović, Aljoscha Burchardt*2011 · 47 citations*](https://md.research.elamur.ai/free/en/papers/popovic-2011-human-automatic-error-classification-machine/)
9. [**Flexible finite-state lexical selection for rule-based machine translation**Francis M. Tyers, Felipe Sánchez-Martínez, Mikel L. Forcada*2012 · 31 citations*](https://md.research.elamur.ai/free/en/papers/tyers-2012-flexible-finite-state-lexical-selection/)
10. [**Spelling Normalization of Historical Documents by Using a Machine Translation Approach**Miguel Domingo, Francisco Casacuberta*2018 · 20 citations*](https://md.research.elamur.ai/free/en/papers/domingo-2018-spelling-normalization-historical-documents-machine/)
11. [**Hebrew Morphological Preprocessing for Statistical Machine Translation**Nimesh Singh, Nizar Habash*2012 · 15 citations*](https://md.research.elamur.ai/free/en/papers/singh-2012-hebrew-morphological-preprocessing-statistical-machine/)
12. [**Apertium: a free/open source platform for machine translation and basic language technology**Mikel L. Forcada, Francis M. Tyers*2016 · 12 citations*](https://md.research.elamur.ai/free/en/papers/forcada-2016-apertium-free-open-source-platform/)
13. [**Grammatical Framework (GF) for MT in Sublanguage Domains**Janna Khegai*2006 · 4 citations*](https://md.research.elamur.ai/free/en/papers/khegai-2006-grammatical-framework-gf-mt-sublanguage/)
14. [**A Longitudinal Study of the Adoption of Specialized MT Systems in Canadian Parliamentary Translation**Michel Simard, Jeniffer Leal-Wyss, Gabriel Bernier-Colborne, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/simard-2026-longitudinal-study-adoption-specialized-mt/)
15. [**Multilingual Communication in the Asylum Context: Evaluating LLM-Based Machine Translation with Fuzzy Match Augmentation and Adaptive NMT across Resource Conditions under Low-Data Constraints**Thomas Moerman, Arda Tezcan, Lieve Macken*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/moerman-2026-multilingual-communication-asylum-context-evaluating/)
16. [**‘It's like talking about how I use a pencil': Journalists' use of machine translation in their work**Mary Nurminen, Nina Havumetsä*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nurminen-2026-it-s-like-talking-about/)
17. [**“All those in favour will please say yea”: Understanding the Factors Behind Machine Translation Adoption at the Canadian Parliament**Jeniffer Leal-Wyss, Delaney Lothian, Gabriel Bernier-Colborne, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/wyss-2026-all-those-favour-will-please/)
18. [**Holistic Regression Testing for High-Quality MT: Some Methodological and Technological Reflections**Stephan Oepen, Helge Dyvik, Dan Flickinger, et al.*2005 · 1 citation*](https://md.research.elamur.ai/free/en/papers/oepen-2005-holistic-regression-testing-high-quality/)
19. [**One Size Does Not Fit All: Why EU Legislative Translation Demands Domain-Specific Fine-Tuning of LLMs**Valerio Lorini, Paula Vlaic, Ulascan Akbulut, et al.*2026*](https://md.research.elamur.ai/free/en/papers/lorini-2026-one-size-does-not-fit/)
20. [**Making Jobs Accessible through AI-supported Easy Language Translation**Fabian Merkel, Marco Baumgartner, Athanasios Breskas, et al.*2026*](https://md.research.elamur.ai/free/en/papers/merkel-2026-making-jobs-accessible-through-ai/)
21. [**Meaning-Making Process and Error Dynamics in ChatGPT-Mediated Translation**Iulia Mihalache, María-José Varela Salinas*2026*](https://md.research.elamur.ai/free/en/papers/mihalache-2026-meaning-making-process-error-dynamics/)
22. [**On the Use of LLMs for Specialised Terminology: A Good Alternative to Corpora?**Joachim Minder, Guillaume Wisniewski, Natalie Kübler*2026*](https://md.research.elamur.ai/free/en/papers/minder-2026-use-llms-specialised-terminology-good/)
23. [**Beyond Simple Term Injection: Reasoning Models for Legal Translation in a Non-Dominant Language Variety**Paolo Di Natale, Elena Chiocchetti, Marlies Alber, et al.*2026*](https://md.research.elamur.ai/free/en/papers/natale-2026-beyond-simple-term-injection-reasoning/)
24. [**When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content**Lydia Nishimwe, Benoît Sagot, Rachel Bawden*2026*](https://md.research.elamur.ai/free/en/papers/nishimwe-2026-when-gold-standard-isn-t/)
25. [**AI Post-Editing in Production: A 71,262-Segment Evaluation Across Five Domains, Ten Languages and Five Systems**Mara Nunziatini, Mercedes Speroni*2026*](https://md.research.elamur.ai/free/en/papers/nunziatini-2026-ai-post-editing-production-71/)
26. [**TaMTAS: Terminology-Aware Machine Translation for Accessible Science. Large Corpus compilation, terminology extraction and data augmentation**Antoni Oliver, Sergi Alvarez-Vidal*2026*](https://md.research.elamur.ai/free/en/papers/oliver-2026-tamtas-terminology-aware-machine-translation/)
27. [**TELÓ: AI-Driven Automatic Subtitling for the Promotion of the Performing Arts**Antoni Oliver, Sílvia Rodríguez Vázquez, Manel Jiménez*2026*](https://md.research.elamur.ai/free/en/papers/oliver-2026-telo-ai-driven-automatic-subtitling/)
28. [**A Multilingual Red Teaming–Driven Safety Analysis of LLMs**Patrícia Pandeiro, Vera Cabarrão, Helena Moniz*2026*](https://md.research.elamur.ai/free/en/papers/pandeiro-2026-multilingual-red-teaming-driven-safety/)
29. [**Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation**Shenbin Qian, Yves Scherrer*2026*](https://md.research.elamur.ai/free/en/papers/qian-2026-why-do-large-language-models/)
30. [**Extending Creativity: Large Language Models and the Practice of Poetry Translation**Natalia Resende, James Hadley*2026*](https://md.research.elamur.ai/free/en/papers/resende-2026-extending-creativity-large-language-models/)
31. [**AIDA Agents: A Multi-Agent Translation Platform with Context-Aware Quality Control**Emanuele Di Rosa, Piotr Peszynski*2026*](https://md.research.elamur.ai/free/en/papers/rosa-2026-aida-agents-multi-agent-translation/)
32. [**Adaptive CAT-embedded MT for low-memory, low-compute end-user devices**Marek Sabo*2026*](https://md.research.elamur.ai/free/en/papers/sabo-2026-adaptive-cat-embedded-mt-low/)
33. [**From Insights to Impact: Actionable Interpretability for Neural Machine Translation**Gabriele Sarti*2026*](https://md.research.elamur.ai/free/en/papers/sarti-2026-insights-impact-actionable-interpretability-neural/)
34. [**Audio description between MT translation and recreation: An Interview Study for the Language Pair English-German**Merle Sauter, Ekaterina Lapshinova-Koltunski, Sylvia Jaki*2026*](https://md.research.elamur.ai/free/en/papers/sauter-2026-audio-description-between-mt-translation/)
35. [**Quality and Comprehensibility of Interlingual Subtitles Produced by Humans or with Machines**Lara Shoana Schlüter, Ekaterina Lapshinova-Koltunski, Sylvia Jaki*2026*](https://md.research.elamur.ai/free/en/papers/schluter-2026-quality-comprehensibility-interlingual-subtitles-produced/)
36. [**BlAInded by Fluency: How Idiomatic Machine Translation Outputs Affect Student Post-Editors' Edit Types**Valentin Scourneau, Loïc De Faria Pires*2026*](https://md.research.elamur.ai/free/en/papers/scourneau-2026-blainded-fluency-how-idiomatic-machine/)
37. [**Multi-Agent Debate for Machine Translation: A Case Study on English-Japanese Translation**Zhan Shen, Jason Naradowsky, Xiaotian Wang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/shen-2026-multi-agent-debate-machine-translation/)
38. [**Diversity and Homogenisation in Generative AI Translation: A Comparative Study of English-Dutch Translation Across Domains**Dimitar Shterionov, Noa van Helleman, Eva Vanmassenhove*2026*](https://md.research.elamur.ai/free/en/papers/shterionov-2026-diversity-homogenisation-generative-ai-translation/)
39. [**Reasoning as Supportive Context for Machine Translation: A Case Study on Hindi to Bengali Language Pair**Kshetrimayum Boynao Singh, Saksham Singh, Partha Pakray, et al.*2026*](https://md.research.elamur.ai/free/en/papers/singh-2026-reasoning-as-supportive-context-machine/)
40. [**Analyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation**David Stap*2026*](https://md.research.elamur.ai/free/en/papers/stap-2026-analyzing-improving-cross-lingual-knowledge/)
41. [**Smarter edits? Post-editing with error highlights and translation suggestions**Fleur V.J. van Tellingen, Gautam Ranka, Dora Žugčić, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tellingen-2026-smarter-edits-post-editing-error/)
42. [**To Write or to Automate Linguistic Prompts, That Is the Question**Marina Sánchez-Torrón, Daria Akselrod, Jason Rauchwerk*2026*](https://md.research.elamur.ai/free/en/papers/torron-2026-write-or-automate-linguistic-prompts/)
43. [**The MaTOS Pipeline for the Translation of Scientific Abstracts on the HAL Platform**Panagiotis Tsolakis, Ziqian Peng, Laurent Romary, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tsolakis-2026-matos-pipeline-translation-scientific-abstracts/)
44. [**Scalable Video-Based Search in the VGT Dictionary**Toon Vandendriessche, Caro Brosens, Hannes De Durpel, et al.*2026*](https://md.research.elamur.ai/free/en/papers/vandendriessche-2026-scalable-video-based-search-vgt/)
45. [**HERMeS: Human Evaluation & Ranking of MultiplE Systems**Rex Vanhorn*2026*](https://md.research.elamur.ai/free/en/papers/vanhorn-2026-hermes-human-evaluation-ranking-multiple/)
46. [**MaTIAS – Machine Translation to Inform Asylum Seekers: final results**July De Wilde, Anaïs Wouters, Arda Tezcan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wilde-2026-matias-machine-translation-inform-asylum/)
47. [**LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry**Xiaojing Yang, Zhihan Li, Gege Sun, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yang-2026-lora-fine-tuning-english-norwegian/)
48. [**LLM-as-a-Jury for Machine Translation Publishability Assessment**Alex Yanishevsky, Olivia Norris*2026*](https://md.research.elamur.ai/free/en/papers/yanishevsky-2026-llm-as-jury-machine-translation/)
49. [**Terminology-Aware Retrieval-Augmented Knowledge Distillation for Biomedical Neural Machine Translation**Maria Zafar, Souhail Bakkali, Rejwanul Haque*2026*](https://md.research.elamur.ai/free/en/papers/zafar-2026-terminology-aware-retrieval-augmented-knowledge/)
50. [**Embedding Similarity Is Not Quality Estimation: Lessons from Replacing a Dedicated QE Model**Dimitrios Zaikis, Andrea Biondo, Matthew Dixon, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zaikis-2026-embedding-similarity-is-not-quality/)
51. [**LocRegen: Cost-Efficient Redundancy Removal in Multilingual E-commerce Titles with Small Language Models**Bryan Zhang, Stephan Walter, Luca Lomanto, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhang-2026-locregen-cost-efficient-redundancy-removal/)
52. [**International translation in the Grammatical Framework (GF)**Aarne Ranta, Kasimir Angelov, Thomas Hallgren, et al.*2016*](https://md.research.elamur.ai/free/en/papers/ranta-2016-international-translation-grammatical-framework-gf/)
53. [**Identifying Complex Phenomena in a Corpus via a Treebank Lens**Dan Flickinger*2006*](https://md.research.elamur.ai/free/en/papers/flickinger-2006-identifying-complex-phenomena-corpus-treebank/)
54. [**A trainable transfer-based MT approach for languages with limited resources**Alon Lavie, Katharina Probst, Erik Peterson, et al.*2004*](https://md.research.elamur.ai/free/en/papers/lavie-2004-trainable-transfer-based-mt-approach/)
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