---
title: "EAMT 2026 — all accepted papers with summaries and citations"
description: "All 89 papers of EAMT 2026, the most cited first; those with our summary link to it. Among them: A Longitudinal Study of the Adoption of…"
canonical: "https://research.elamur.ai/free/en/venues/eamt/2026/papers/"
---
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# EAMT 2026: all accepted papers

89 papers · the most cited first · [← EAMT 2026](https://md.research.elamur.ai/free/en/venues/eamt/2026/)

1. **[A Longitudinal Study of the Adoption of Specialized MT Systems in Canadian Parliamentary Translation](https://md.research.elamur.ai/free/en/papers/simard-2026-longitudinal-study-adoption-specialized-mt/)**Michel Simard, Jeniffer Leal-Wyss, Gabriel Bernier-Colborne et al.*our summary*
2. **[A Multilingual Red Teaming–Driven Safety Analysis of LLMs](https://md.research.elamur.ai/free/en/papers/pandeiro-2026-multilingual-red-teaming-driven-safety/)**Patrícia Pandeiro, Vera Cabarrão, Helena Moniz*our summary*
3. **[AI Post-Editing in Production: A 71,262-Segment Evaluation Across Five Domains, Ten Languages and Five Systems](https://md.research.elamur.ai/free/en/papers/nunziatini-2026-ai-post-editing-production-71/)**Mara Nunziatini, Mercedes Speroni*our summary*
4. **[AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documents ↗](https://aclanthology.org/2026.eamt-1.49/)**Vicent Briva-Iglesias, María Ferre Fernández
5. **[AIDA Agents: A Multi-Agent Translation Platform with Context-Aware Quality Control](https://md.research.elamur.ai/free/en/papers/rosa-2026-aida-agents-multi-agent-translation/)**Emanuele Di Rosa, Piotr Peszynski*our summary*
6. **[ARTICULATE: Science in your Own Language ↗](https://aclanthology.org/2026.eamt-2.7/)**Yolanda Vazquez-Alvarez, Matthew P. Aylett, Benjamin R. Cowan et al.
7. **[Adaptive CAT-embedded MT for low-memory, low-compute end-user devices](https://md.research.elamur.ai/free/en/papers/sabo-2026-adaptive-cat-embedded-mt-low/)**Marek Sabo*our summary*
8. **[Advanced CAT Tool Features for Enhancing Consistency in MT and Generative AI Outputs ↗](https://aclanthology.org/2026.eamt-2.17/)**Judith Klein
9. **[Advancing Medical Communication: Multilingual, Multicultural, and Multimodal Processing for Translation and Simplification ↗](https://aclanthology.org/2026.eamt-2.21/)**Maria Pia Di Buono
10. **[Alignment Quality Degradation Across the Parallel–Comparable Spectrum: A Comparative Analysis ↗](https://aclanthology.org/2026.eamt-1.11/)**Audrey Mash, Jonathan Ayebakuro Orama, Marc Juvillà Garcia et al.
11. **[Analyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation](https://md.research.elamur.ai/free/en/papers/stap-2026-analyzing-improving-cross-lingual-knowledge/)**David Stap*our summary*
12. **[Artificial intelligence language technologies in multilingual healthcare: Grand challenges ahead ↗](https://aclanthology.org/2026.eamt-1.48/)**Vicent Briva-Iglesias
13. **[Audio description between MT translation and recreation: An Interview Study for the Language Pair English-German](https://md.research.elamur.ai/free/en/papers/sauter-2026-audio-description-between-mt-translation/)**Merle Sauter, Ekaterina Lapshinova-Koltunski, Sylvia Jaki*our summary*
14. **[Augmenting Text to Increase Translation Difficulty ↗](https://aclanthology.org/2026.eamt-1.21/)**William Kalikman, Simon Sukup, Michal Tešnar et al.
15. **[Automated Information Extraction and Template Filling from Client Style Guides ↗](https://aclanthology.org/2026.eamt-2.31/)**Leonor Graça, Vera Cabarrão, Helena Moniz
16. **[Automatic translation in public services: A survey of the Finnish public sector ↗](https://aclanthology.org/2026.eamt-1.45/)**Sıla Ilkılıç, Maarit Koponen, Mary Nurminen
17. **[Beyond Semantics: Measuring Fine-Grained Emotion Preservation in Small Language Model-Based Machine Translation ↗](https://aclanthology.org/2026.eamt-1.24/)**Dawid Wiśniewski, Igor Czudy
18. **[Beyond Simple Term Injection: Reasoning Models for Legal Translation in a Non-Dominant Language Variety](https://md.research.elamur.ai/free/en/papers/natale-2026-beyond-simple-term-injection-reasoning/)**Paolo Di Natale, Elena Chiocchetti, Marlies Alber et al.*our summary*
19. **[BlAInded by Fluency: How Idiomatic Machine Translation Outputs Affect Student Post-Editors' Edit Types](https://md.research.elamur.ai/free/en/papers/scourneau-2026-blainded-fluency-how-idiomatic-machine/)**Valentin Scourneau, Loïc De Faria Pires*our summary*
20. **[Bridging Domains for Automatic Post-Editing: A Classifier-Guided Multi-Domain Adaptation Framework ↗](https://aclanthology.org/2026.eamt-1.31/)**Sourabh Deoghare, Diptesh Kanojia, Pushpak Bhattacharyya
21. **[CRITICS: Critical Science Without Borders by Translation of Scientific Knowledge ↗](https://aclanthology.org/2026.eamt-2.23/)**Rodrigo Agerri, Itziar Aldabe, Elena Cabrio et al.
22. **[Can professional translators identify machine-generated text? ↗](https://aclanthology.org/2026.eamt-1.35/)**Michael Farrell
23. **[CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs ↗](https://aclanthology.org/2026.eamt-1.9/)**Kamil Guttmann, Zofia Fraś, Artur Nowakowski et al.
24. **[Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations ↗](https://aclanthology.org/2026.eamt-1.43/)**Kyo Gerrits, Rik van Noord, Ana Guerberof Arenas
25. **[DA + Criteria: A New Quality Assessment Method for Bridging the Gap Between Human and Machine Translation ↗](https://aclanthology.org/2026.eamt-2.13/)**Bettina Hiebl
26. **[Diversity and Homogenisation in Generative AI Translation: A Comparative Study of English-Dutch Translation Across Domains](https://md.research.elamur.ai/free/en/papers/shterionov-2026-diversity-homogenisation-generative-ai-translation/)**Dimitar Shterionov, Noa van Helleman, Eva Vanmassenhove*our summary*
27. **[Diversity-Aware Literary Machine Translation with Multi-Reward Policy Optimization ↗](https://aclanthology.org/2026.eamt-1.17/)**Zeynep Yirmibeşoğlu Balal, Tunga Güngör
28. **[Does Speech Translation Meet Users' Needs? An English to Portuguese Study Across Demographics ↗](https://aclanthology.org/2026.eamt-2.22/)**Giuseppe Attanasio, Beatrice Savoldi, Daniel Chechelnitsky et al.
29. **[Embedding Similarity Is Not Quality Estimation: Lessons from Replacing a Dedicated QE Model](https://md.research.elamur.ai/free/en/papers/zaikis-2026-embedding-similarity-is-not-quality/)**Dimitrios Zaikis, Andrea Biondo, Matthew Dixon et al.*our summary*
30. **[English–Nepali–Tamang: A Trilingual Parallel Corpus and Benchmark for Low-Resource Machine Translation ↗](https://aclanthology.org/2026.eamt-2.11/)**Praveen Acharya, Rupak Raj Ghimire, Prakash Poudyal et al.
31. **[Enhancing LLM Translation Performance for Spanish–Valencian through Supervised Fine-Tuning and Reinforcement Learning ↗](https://aclanthology.org/2026.eamt-2.27/)**Paula Guerrero Castelló
32. **[Evaluating Machine Translation and Automatic Metrics in Subtitling: A Case Study on Spanish Multiword Expressions ↗](https://aclanthology.org/2026.eamt-1.29/)**María Miró Maestre, Iván Martínez-Murillo
33. **[Evaluating Terminology Translation Methods ↗](https://aclanthology.org/2026.eamt-1.28/)**Théo Salmenkivi-Friberg, Iikka Hauhio, Tommi Nieminen
34. **[Evaluating the Effect of Prompt Language on LLM-based Translation: Evidence from Spanish\ensuremath\<\ensuremath>Italian Translation ↗](https://aclanthology.org/2026.eamt-1.51/)**Antonella Bove, Paola Di Cataldo, Davide Maestroni
35. **[Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution ↗](https://aclanthology.org/2026.eamt-1.18/)**Janiça Hackenbuchner, Jasper Degraeuwe, Arda Tezcan et al.
36. **[Extending Creativity: Large Language Models and the Practice of Poetry Translation](https://md.research.elamur.ai/free/en/papers/resende-2026-extending-creativity-large-language-models/)**Natalia Resende, James Hadley*our summary*
37. **[ForMaT: Dataset for Visually-Grounded Multilingual PDF Translation ↗](https://aclanthology.org/2026.eamt-1.12/)**Michał Ciesiółka, Dawid Wiśniewski, Adrian Charkiewicz et al.
38. **[From Insights to Impact: Actionable Interpretability for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/sarti-2026-insights-impact-actionable-interpretability-neural/)**Gabriele Sarti*our summary*
39. **[Fuzzy Matching and Sentence Embeddings for Few-shot Machine Translation with Large Language Models ↗](https://aclanthology.org/2026.eamt-1.34/)**Miguel Angel Rios Gaona, Claudia Plieseis, Dragos Ciobanu et al.
40. **[HERMeS: Human Evaluation & Ranking of MultiplE Systems](https://md.research.elamur.ai/free/en/papers/vanhorn-2026-hermes-human-evaluation-ranking-multiple/)**Rex Vanhorn*our summary*
41. **[Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data ↗](https://aclanthology.org/2026.eamt-1.27/)**Maxime Bouthors, Josep Crego, Dakun Zhang et al.
42. **[IndicDISCO-MT: A Discourse-Centric Benchmark for Evaluating Discourse Phenomena in Indian Language Machine Translation ↗](https://aclanthology.org/2026.eamt-1.15/)**Heli Hingrajiya, Vennela Bairi, Vandan Mujadia et al.
43. **[Is a Picture Worth a Thousand Words? Exploration and Implementation Considerations for Visual Context in Translation Workflows ↗](https://aclanthology.org/2026.eamt-2.29/)**Vera Senderowicz Guerra, Olesia Khrapunova
44. **[LLM-as-a-Jury for Machine Translation Publishability Assessment](https://md.research.elamur.ai/free/en/papers/yanishevsky-2026-llm-as-jury-machine-translation/)**Alex Yanishevsky, Olivia Norris*our summary*
45. **[Literacy-Grounded and Industry-Oriented Translation Training with LT-LiDER ↗](https://aclanthology.org/2026.eamt-2.1/)**Janiça Hackenbuchner, María Isabel Rivas Ginel, Joss Moorkens et al.
46. **[LoRA Fine-Tuning of English–Norwegian NMT for the Oil & Gas Industry](https://md.research.elamur.ai/free/en/papers/yang-2026-lora-fine-tuning-english-norwegian/)**Xiaojing Yang, Zhihan Li, Gege Sun et al.*our summary*
47. **[LocRegen: Cost-Efficient Redundancy Removal in Multilingual E-commerce Titles with Small Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locregen-cost-efficient-redundancy-removal/)**Bryan Zhang, Stephan Walter, Luca Lomanto et al.*our summary*
48. **[MaTIAS – Machine Translation to Inform Asylum Seekers: final results](https://md.research.elamur.ai/free/en/papers/wilde-2026-matias-machine-translation-inform-asylum/)**July De Wilde, Anaïs Wouters, Arda Tezcan et al.*our summary*
49. **[Machine Translation in the Wild: User Reaction to Xiaohongshu's Built-In Translation Feature ↗](https://aclanthology.org/2026.eamt-1.52/)**Sui He
50. **[Making Jobs Accessible through AI-supported Easy Language Translation](https://md.research.elamur.ai/free/en/papers/merkel-2026-making-jobs-accessible-through-ai/)**Fabian Merkel, Marco Baumgartner, Athanasios Breskas et al.*our summary*
51. **[Meaning-Making Process and Error Dynamics in ChatGPT-Mediated Translation](https://md.research.elamur.ai/free/en/papers/mihalache-2026-meaning-making-process-error-dynamics/)**Iulia Mihalache, María-José Varela Salinas*our summary*
52. **[MetaDocEval: A Contrastive Framework for Evaluating Machine Translation Metrics at the Document-Level ↗](https://aclanthology.org/2026.eamt-1.19/)**Nicolas Dahan, Rachel Bawden, François Yvon
53. **[Metaphors in Literary Post-Editing: Opening Pandora's Box? ↗](https://aclanthology.org/2026.eamt-1.47/)**Aletta G. Dorst, Mayra O. Nas, Katinka Zeven
54. **[Mitigating Gender Bias in English-Ukrainian Machine Translation Models ↗](https://aclanthology.org/2026.eamt-1.14/)**Pavels Ivanovs, Gina Welsh, Irini Selenica
55. **[Multi-Agent Debate for Machine Translation: A Case Study on English-Japanese Translation](https://md.research.elamur.ai/free/en/papers/shen-2026-multi-agent-debate-machine-translation/)**Zhan Shen, Jason Naradowsky, Xiaotian Wang et al.*our summary*
56. **[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](https://md.research.elamur.ai/free/en/papers/moerman-2026-multilingual-communication-asylum-context-evaluating/)**Thomas Moerman, Arda Tezcan, Lieve Macken*our summary*
57. **[OSCAIL-OpenScience Communication through AI in EU Languages ↗](https://aclanthology.org/2026.eamt-2.3/)**Sheila Castilho, Susanna Fiorini, Lynne Bowker et al.
58. **[On the Use of LLMs for Specialised Terminology: A Good Alternative to Corpora?](https://md.research.elamur.ai/free/en/papers/minder-2026-use-llms-specialised-terminology-good/)**Joachim Minder, Guillaume Wisniewski, Natalie Kübler*our summary*
59. **[One Size Does Not Fit All: Why EU Legislative Translation Demands Domain-Specific Fine-Tuning of LLMs](https://md.research.elamur.ai/free/en/papers/lorini-2026-one-size-does-not-fit/)**Valerio Lorini, Paula Vlaic, Ulascan Akbulut et al.*our summary*
60. **[Parallel Corpus Development Toolkit (PCDT): A Web-Based Platform for Multilingual Parallel Data Creation ↗](https://aclanthology.org/2026.eamt-2.10/)**Praveen Acharya, Rupak Raj Ghimire, Bipesh Subedi et al.
61. **[Presentation of the Project CLingS: Cross-lingual information retrieval for scientific datasets in less-resourced languages ↗](https://aclanthology.org/2026.eamt-2.6/)**Valentina Fedchenko, Ka-I Lim, Milan Rusko
62. **[Prompsit's API and CLI: planet-friendly, privacy-first, open-source translation services for everyone ↗](https://aclanthology.org/2026.eamt-2.20/)**Lev Nikolaevich Berezhnoy, Gema Ramírez Sánchez, Sergio Ortiz Rojas et al.
63. **[Quality and Comprehensibility of Interlingual Subtitles Produced by Humans or with Machines](https://md.research.elamur.ai/free/en/papers/schluter-2026-quality-comprehensibility-interlingual-subtitles-produced/)**Lara Shoana Schlüter, Ekaterina Lapshinova-Koltunski, Sylvia Jaki*our summary*
64. **[Quebec Translators in the Age of AI: Perceptions on the Evolution and Sustainability of the Translation Profession ↗](https://aclanthology.org/2026.eamt-1.54/)**Lynne Bowker, Monyka L. Rodrigues
65. **[Reaching multilingual communities: a survey mapping MT use in the West Midlands (UK) third and public sector organisations ↗](https://aclanthology.org/2026.eamt-1.38/)**David Orrego-Carmona, Priyanki Ghosh, Susana Valdez
66. **[Reasoning as Supportive Context for Machine Translation: A Case Study on Hindi to Bengali Language Pair](https://md.research.elamur.ai/free/en/papers/singh-2026-reasoning-as-supportive-context-machine/)**Kshetrimayum Boynao Singh, Saksham Singh, Partha Pakray et al.*our summary*
67. **[Scalable Video-Based Search in the VGT Dictionary](https://md.research.elamur.ai/free/en/papers/vandendriessche-2026-scalable-video-based-search-vgt/)**Toon Vandendriessche, Caro Brosens, Hannes De Durpel et al.*our summary*
68. **[SinMix2Mono: A Dataset for Code-mixed Romanized Sinhala Translation and Transliteration ↗](https://aclanthology.org/2026.eamt-1.10/)**Rukshan Dias, Deshan Sumanathilaka, Archchana Sindhujan et al.
69. **[Smarter edits? Post-editing with error highlights and translation suggestions](https://md.research.elamur.ai/free/en/papers/tellingen-2026-smarter-edits-post-editing-error/)**Fleur V.J. van Tellingen, Gautam Ranka, Dora Žugčić et al.*our summary*
70. **[TELÓ: AI-Driven Automatic Subtitling for the Promotion of the Performing Arts](https://md.research.elamur.ai/free/en/papers/oliver-2026-telo-ai-driven-automatic-subtitling/)**Antoni Oliver, Sílvia Rodríguez Vázquez, Manel Jiménez*our summary*
71. **[TaMTAS: Terminology-Aware Machine Translation for Accessible Science. Large Corpus compilation, terminology extraction and data augmentation](https://md.research.elamur.ai/free/en/papers/oliver-2026-tamtas-terminology-aware-machine-translation/)**Antoni Oliver, Sergi Alvarez-Vidal*our summary*
72. **[Terminology-Aware Retrieval-Augmented Knowledge Distillation for Biomedical Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/zafar-2026-terminology-aware-retrieval-augmented-knowledge/)**Maria Zafar, Souhail Bakkali, Rejwanul Haque*our summary*
73. **[The Challenge of Finding Robust and Efficient Strategies for Training Machine Translation Models with Noisy Data ↗](https://aclanthology.org/2026.eamt-1.13/)**Mikko Aulamo, Sami Virpioja, Yves Scherrer et al.
74. **[The MULTI-TRAD Project: Parallel Corpora and Multidimensional Analysis of Human, Machine and Post-Edited Translation in the Third Social Sector ↗](https://aclanthology.org/2026.eamt-2.9/)**Maria del Mar Sánchez Ramos, Douglas E. Biber, Cristina Cano Fernández et al.
75. **[The MaTOS Pipeline for the Translation of Scientific Abstracts on the HAL Platform](https://md.research.elamur.ai/free/en/papers/tsolakis-2026-matos-pipeline-translation-scientific-abstracts/)**Panagiotis Tsolakis, Ziqian Peng, Laurent Romary et al.*our summary*
76. **[The Potential of Large Language Models for Translating Tourism Promotional Texts: A Mixed-methods Study ↗](https://aclanthology.org/2026.eamt-1.39/)**Raghad Alsulami
77. **[The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish–Chinese Journalistic Translation ↗](https://aclanthology.org/2026.eamt-1.57/)**Haohong Lai, Weijia Li
78. **[The Two Towers for Estonian-Centric and Finno-Ugric Machine Translation ↗](https://aclanthology.org/2026.eamt-1.32/)**Mark Fishel, Lisa Yankovskaya
79. **[To Write or to Automate Linguistic Prompts, That Is the Question](https://md.research.elamur.ai/free/en/papers/torron-2026-write-or-automate-linguistic-prompts/)**Marina Sánchez-Torrón, Daria Akselrod, Jason Rauchwerk*our summary*
80. **[Towards Visually-Guided Movie Subtitle Translation for Indic Languages ↗](https://aclanthology.org/2026.eamt-2.28/)**Tarun Chintada, Kshetrimayum Boynao Singh, Asif Ekbal
81. **[Translating Under Pressure: Domain-Aware LLMs for Crisis Communication ↗](https://aclanthology.org/2026.eamt-1.5/)**Antonio Castaldo, Maria Carmen Staiano, Johanna Monti et al.
82. **[Translation 2.0: Equipping linguists for the machine translation future ↗](https://aclanthology.org/2026.eamt-2.18/)**Alina Karakanta, Vasilis Kalogiannis
83. **[Translation Analytics for Freelancers II: Benchmarking Local LLMs for Confidential Translation Workflows ↗](https://aclanthology.org/2026.eamt-1.37/)**Yuri Balashov, Rex Vanhorn, Mingxi Xu et al.
84. **[Using Model Disagreement to Identify Unstable Regions in MT Evaluation ↗](https://aclanthology.org/2026.eamt-1.22/)**Vitalii Iakivchuk
85. **[VERA: A Platform for Automatic and Human Evaluation of Machine Translation ↗](https://aclanthology.org/2026.eamt-2.5/)**Sofía García González, Inés Quintana Raña, Jorge N. Afonso Cabido et al.
86. **[When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content](https://md.research.elamur.ai/free/en/papers/nishimwe-2026-when-gold-standard-isn-t/)**Lydia Nishimwe, Benoît Sagot, Rachel Bawden*our summary*
87. **[Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation](https://md.research.elamur.ai/free/en/papers/qian-2026-why-do-large-language-models/)**Shenbin Qian, Yves Scherrer*our summary*
88. **[`It's like talking about how I use a pencil': Journalists' use of machine translation in their work](https://md.research.elamur.ai/free/en/papers/nurminen-2026-it-s-like-talking-about/)**Mary Nurminen, Nina Havumetsä*our summary*
89. **[“All those in favour will please say yea”: Understanding the Factors Behind Machine Translation Adoption at the Canadian Parliament](https://md.research.elamur.ai/free/en/papers/wyss-2026-all-those-favour-will-please/)**Jeniffer Leal-Wyss, Delaney Lothian, Gabriel Bernier-Colborne et al.*our summary*
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