---
title: "Who cites Mohammad Amin Farajian"
description: "571 papers of the Elamur Research library cite the works of Mohammad Amin Farajian, found in their bibliographies; the most cited first."
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---
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# Who cites Mohammad Amin Farajian

627 citations of their works from 571 papers of our library (of the 140,884 whose bibliographies we read) · 45 with a page here, the most cited first · [← Mohammad Amin Farajian](https://md.research.elamur.ai/free/en/authors/mohammad-amin-farajian/)

1. [**COMET: A Neural Framework for MT Evaluation**Ricardo Rei, Craig Stewart, Ana C Farinha, et al.*2020 · 1,784 citations*](https://md.research.elamur.ai/free/en/papers/rei-2020-comet-neural-framework-mt-evaluation/)
2. [**Simple, Scalable Adaptation for Neural Machine Translation**Ankur Bapna, Orhan Firat*2019 · 524 citations*](https://md.research.elamur.ai/free/en/papers/bapna-2019-simple-scalable-adaptation-neural-machine/)
3. [**Findings of the 2020 Conference on Machine Translation (WMT20)**Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, et al.*2020 · 401 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2020-findings-2020-conference-machine-translation/)
4. [**Unsupervised Domain Clusters in Pretrained Language Models**Roee Aharoni, Yoav Goldberg*2020 · 326 citations*](https://md.research.elamur.ai/free/en/papers/aharoni-2020-unsupervised-domain-clusters-pretrained-language/)
5. [**A Survey of Domain Adaptation for Neural Machine Translation**Chenhui Chu, Rui Wang*2018 · 304 citations*](https://md.research.elamur.ai/free/en/papers/chu-2018-survey-domain-adaptation-neural-machine/)
6. [**Training Neural Machine Translation to Apply Terminology Constraints**Georgiana Dinu, Prashant Mathur, Marcello Federico, et al.*2019 · 242 citations*](https://md.research.elamur.ai/free/en/papers/dinu-2019-training-neural-machine-translation-apply/)
7. [**OpenKiwi: An Open Source Framework for Quality Estimation**Fabio Kepler, Jonay Trénous, Marcos Treviso, et al.*2019 · 215 citations*](https://md.research.elamur.ai/free/en/papers/kepler-2019-openkiwi-open-source-framework-quality/)
8. [**Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem**Danielle Saunders, Bill Byrne*2020 · 196 citations*](https://md.research.elamur.ai/free/en/papers/saunders-2020-reducing-gender-bias-neural-machine/)
9. [**Findings of the WMT 2020 Shared Task on Quality Estimation**Lucia Specia, Frédéric Blain, Marina Fomicheva, et al.*2020 · 160 citations*](https://md.research.elamur.ai/free/en/papers/specia-2020-findings-wmt-2020-shared-task-2020-wmt-1-79/)
10. [**Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist**Marzena Karpinska, Mohit Iyyer*2023 · 156 citations*](https://md.research.elamur.ai/free/en/papers/karpinska-2023-large-language-models-effectively-leverage/)
11. [**Findings of the WMT 2019 Shared Task on Automatic Post-Editing**Rajen Chatterjee, Christian Federmann, Matteo Negri, et al.*2019 · 102 citations*](https://md.research.elamur.ai/free/en/papers/chatterjee-2019-findings-wmt-2019-shared-task/)
12. [**Unbabel's Participation in the WMT20 Metrics Shared Task**Ricardo Rei, Craig Stewart, Ana C Farinha, et al.*2020 · 98 citations*](https://md.research.elamur.ai/free/en/papers/rei-2020-unbabel-s-participation-wmt20-metrics/)
13. [**Findings of the WMT25 General Machine Translation Shared Task: Time to Stop Evaluating on Easy Test Sets**Tom Kocmi, Ekaterina Artemova, Eleftherios Avramidis, et al.*2025 · 73 citations*](https://md.research.elamur.ai/free/en/papers/kocmi-2025-findings-wmt25-general-machine-translation/)
14. [**Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric into a Document-Level Metric**Giorgos Vernikos, Brian Thompson, Prashant Mathur, et al.*2022 · 68 citations*](https://md.research.elamur.ai/free/en/papers/vernikos-2022-embarrassingly-easy-document-level-mt/)
15. [**DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence**Wei Zhao, Michael Strube, Steffen Eger*2023 · 53 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-discoscore-evaluating-text-generation-bert/)
16. [**Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts**Eleftheria Briakou, Jiaming Luo, Colin Cherry, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/briakou-2024-translating-step-step-decomposing-translation/)
17. [**Findings of the WMT 2020 Shared Task on Chat Translation**M. Amin Farajian, António V. Lopes, André F. T. Martins, et al.*2020 · 29 citations*](https://md.research.elamur.ai/free/en/papers/farajian-2020-findings-wmt-2020-shared-task/)
18. [**MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators**Qingyu Lu, Liang Ding, Kanjian Zhang, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/lu-2025-mqm-ape-high-quality-error/)
19. [**Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics**Stefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/perrella-2024-beyond-correlation-interpretable-evaluation-machine/)
20. [**TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs**Ezgi Başar, Francesca Padovani, Jaap Jumelet, et al.*2025 · 14 citations*](https://md.research.elamur.ai/free/en/papers/basar-2025-turblimp-turkish-benchmark-linguistic-minimal/)
21. [**DiBiMT: A Gold Evaluation Benchmark for Studying Lexical Ambiguity in Machine Translation**Federico Martelli, Stefano Perrella, Niccolò Campolungo, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/martelli-2025-dibimt-gold-evaluation-benchmark-studying/)
22. [**The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology**Shahad Al-Khalifa; Nadir Durrani; Hend Al-Khalifa;, Firoj Alam*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/al-khalifa-2025-landscape-arabic-large-language-models/)
23. [**Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation**Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/ojastu-2026-estonian-winogrande-dataset-comparative-analysis/)
24. [**Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures**Chiara Manna, Hosein Mohebbi, Afra Alishahi, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/manna-2026-gender-disambiguation-machine-translation-diagnostic/)
25. [**PHEB: An European Portuguese High School-Level LLM Benchmark**Diogo C. Tavares, Rafael Ferreira, Afonso Simplício, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/tavares-2026-pheb-european-portuguese-high-school/)
26. [**Anchoring the Judge: Curriculum-Based Adaptation and Reference-Anchored MQM for LLM-Based Machine Translation of an Unseen Low-Resource Language - A Case of Nupe**Umar Baba Umar, Sulaimon Adebayo Bashir, Abdulmalik Danlami Mohammed*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/umar-2026-anchoring-judge-curriculum-based-adaptation/)
27. [**Biases in Translation: Assessing Opinion Distortion in Machine Translated Texts**Nazanin Shafiabadi, François Yvon*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/shafiabadi-2026-biases-translation-assessing-opinion-distortion/)
28. [**Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?**Aishwarya Ramasethu, Rohin Garg, Niyathi Allu, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ramasethu-2026-can-linguistically-related-languages-guide/)
29. [**Can LLMs Translate Italy's Language Varieties?**Edoardo Signoroni, Pavel Rychlý*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/signoroni-2026-can-llms-translate-italy-s/)
30. [**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/)
31. [**Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026**Enes Yavuz Ugan, Maike Züfle, Yuka Ko, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ugan-2026-multilingual-long-form-speech-instruction/)
32. [**Speak in Context: Multilingual ASR with Speech–Context Alignment via Contrastive Learning**Yuchen Zhang, Haralambos Mouratidis, Ravi Shekhar*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zhang-2026-speak-context-multilingual-asr-speech/)
33. [**When Translations Surprise: Human Awareness of Predictability in Translations**Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sanchez-Martinez*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/romero-2026-when-translations-surprise-human-awareness/)
34. [**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/)
35. [**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/)
36. [**Cultural and Knowledge Biases in LLMs through the Lens of Entity-Aware Machine Translation**Lu Xu, Luca Moroni, Roberto Navigli*2026*](https://md.research.elamur.ai/free/en/papers/xu-2026-cultural-knowledge-biases-llms-through/)
37. [**EIFFEL: a novel benchmark to measure bias of English heavy training on French idiomatic expressions**Charlotte Noel, Nicholas Asher, Olivier Gouvert, et al.*2026*](https://md.research.elamur.ai/free/en/papers/noel-2026-eiffel-novel-benchmark-measure-bias/)
38. [**Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/)
39. [**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/)
40. [**Reading Time in the Wild: An Assessment of Readability Predictors Based on Naturally-Observed Reading Times**Sijbren van Vaals, Rik van Noord, Malvina Nissim*2026*](https://md.research.elamur.ai/free/en/papers/vaals-2026-reading-time-wild-assessment-readability/)
41. [**Rethinking Gender Annotation for Bias Evaluation in Machine Translation: Can LLMs Improve Reliability?**Chiara Manna, Argentina Anna Rescigno, Eva Vanmassenhove*2026*](https://md.research.elamur.ai/free/en/papers/manna-2026-rethinking-gender-annotation-bias-evaluation/)
42. [**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/)
43. [**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/)
44. [**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/)
45. [**Survey on Document-level Neural Machine Translation (文档级神经机器翻译综述)**Xinglin Lv (吕星林), Junhui Li (李军辉), Shimin Tao (陶仕敏), et al.*2024*](https://md.research.elamur.ai/free/en/papers/lv-2024-document-level-neural/)

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