Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers

BLEU: a method for automatic evaluation of machine translation

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works

Untitled work

(2005) · Cited by 4,152 · see citing works

Moving to a World Beyond “ <i>p</i>  &lt; 0.05”

Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019) · The American Statistician · doi:10.1080/00031305.2019.1583913 · Cited by 2,976 · see citing works

A Study of Translation Edit Rate with Targeted Human Annotation

Snover, M., Dorr, B. J., Schwartz, R. M., Micciulla, L., & Makhoul, J. I. (2006) · Cited by 2,380 · see citing works

A Call for Clarity in Reporting BLEU Scores

Post, M. (2018) · Proceedings of the Third Conference on Machine Translation: Research Papers · doi:10.18653/v1/w18-6319 · Cited by 2,112 · see citing works

Statistical Significance Tests for Machine Translation Evaluation.

Koehn, P. (2004) · Cited by 1,456 · see citing works

chrF: character n-gram F-score for automatic MT evaluation

Popović, M. (2015) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w15-3049 · Cited by 1,026 · see citing works

Re-evaluating the Role of Bleu in Machine Translation Research

Callison-Burch, C., Osborne, M., & Koehn, P. (2006) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 632 · see citing works

A Structured Review of the Validity of BLEU

Reiter, E. (2018) · Computational Linguistics · doi:10.1162/coli_a_00322 · Cited by 386 · see citing works

The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing

Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1128 · Cited by 384 · see citing works

Automatic Evaluation of Translation Quality for Distant Language Pairs

Isozaki, H., Hirao, T., Duh, K., Sudoh, K., & Tsukada, H. (2010) · Cited by 286 · see citing works

Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics

Mathur, N., Baldwin, T., & Cohn, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.448 · Cited by 206 · see citing works

Untitled work

(2005) · Cited by 189 · see citing works

Findings of the 2020 Conference on Machine Translation (WMT20)

Barrault, L., Biesialska, M., Bojar, O., Costa‐jussà, M. R., Federmann, C., Graham, Y., Grundkiewicz, R., Haddow, B., Huck, M., Joanis, E., et al. (2020) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2020.wmt-1.1 · Cited by 178 · see citing works

Selective Attention for Context-aware Neural Machine Translation

Maruf, S., Martins, A. F. T., & Haffari, G. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1313 · Cited by 168 · see citing works

Stronger Baselines for Trustable Results in Neural Machine Translation

Denkowski, M., & Neubig, G. (2017) · doi:10.18653/v1/w17-3203 · Cited by 111 · see citing works

Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning

Kang, X., Zhao, Y., Zhang, J., & Zong, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.175 · Cited by 54 · see citing works

A Set of Recommendations for Assessing Human–Machine Parity in Language Translation

Läubli, S., Castilho, S., Neubig, G., Sennrich, R., Shen, Q., & Toral, A. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-188217 · Cited by 54 · see citing works

Leakage and the reproducibility crisis in machine-learning-based science

Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387 · see citing works

The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation

Goyal, N., Gao, C., Chaudhary, V., Chen, P., Wenzek, G., Ju, D., Krishnan, S., Ranzato, M., Guzmán, F., & Fan, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00474 · Cited by 209 · see citing works

To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine TranslationPreprint

Kocmi, T., Federmann, C., Grundkiewicz, R., Junczys-Dowmunt, M., Matsushita, H., & Menezes, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10821 · Cited by 114 · see citing works

Democratizing neural machine translation with OPUS-MT

Tiedemann, J., Aulamo, M., Bakshandaeva, D., Boggia, M., Grönroos, S.-A., Nieminen, T., Raganato, A., Scherrer, Y., Vázquez, R., & Virpioja, S. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09704-w · Cited by 81 · see citing works

x comet : Transparent Machine Translation Evaluation through Fine-grained Error Detection

Guerreiro, N. M., Rei, R., van Stigt, D., Coheur, L., Colombo, P., & Martins, A. F. T. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00683 · Cited by 108 · see citing works

A global analysis of metrics used for measuring performance in natural language processing

Blagec, K., Dorffner, G., Moradi, M., Ott, S., & Matthias Samwald, A. (2022) · doi:10.18653/v1/2022.nlppower-1.6 · Cited by 42 · see citing works

An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score

Ghassemiazghandi, M. (2024) · Theory and Practice in Language Studies · doi:10.17507/tpls.1404.07 · Cited by 30 · see citing works

Considerations for meaningful sign language machine translation based on glosses

Müller, M., Jiang, Z., Moryossef, A., Rios, A., & Ebling, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.60 · Cited by 31 · see citing works

DEMETR: Diagnosing Evaluation Metrics for Translation

Karpinska, M., Raj, N., Thai, K., Song, Y., Gupta, A., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.649 · Cited by 22 · see citing works

A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels

Joyce, R. J., Raff, E., & Nicholas, C. (2021) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3474369.3486867 · Cited by 19 · see citing works

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Kasai, J., Sakaguchi, K., Bras, R. J. L., Dunagan, L., Morrison, J., Fabbri, A. R., Choi, Y., & Smith, N. A. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.259 · Cited by 19 · see citing works

Rogue Scores

Grusky, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.107 · Cited by 16 · see citing works

UScore: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation

Belouadi, J., & Eger, S. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.27 · Cited by 15 · see citing works

Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with AdaptersPreprint

Stickland, A. C., Bérard, A., & Nikoulina, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.09574 · Cited by 16 · see citing works

Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings

Krishna, K., Nathani, D., Garcia, X., Samanta, B., & Talukdar, P. P. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.514 · Cited by 20 · see citing works

Reproducibility in NLP: What Have We Learned from the Checklist?

Magnusson, I., Smith, N. A., & Dodge, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.809 · Cited by 14 · see citing works

Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation

Zhu, Y., Sun, Z., Cheng, S., Huang, L., Wu, L., & Wang, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.168 · Cited by 14 · see citing works

Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors

Kaster, M., Zhao, W., & Eger, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.701 · Cited by 13 · see citing works

DataLab: A Platform for Data Analysis and Intervention

Yang, X., Fu, J., Yuan, W., Viswanathan, V., Liu, Z., Liu, Y., Neubig, G., & Liu, P. (2022) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2022.acl-demo.18 · Cited by 10 · see citing works

Reproducibility Issues for BERT-based Evaluation Metrics

Chen, Y., Belouadi, J., & Eger, S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.192 · Cited by 10 · see citing works

Toward Human-Like Evaluation for Natural Language Generation with Error Analysis

Lu, Q., Ding, L., Xie, L., Zhang, K., Wong, D. F., & Tao, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.324 · Cited by 13 · see citing works

BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training

Yan, Y., Wang, T., Zhao, C., Huang, S., Chen, J., & Wang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.297 · Cited by 16 · see citing works

Non-Autoregressive Neural Machine Translation: A Call for Clarity

Schmidt, R. M., Pires, T., Peitz, S., & Lööf, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.179 · Cited by 10 · see citing works

NLP in Action

Al-Saggaf, M. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0060-3.ch007 · Cited by 8 · see citing works

Can machine translation match human expertise? Quantifying the performance of large language models in the translation of patient-reported outcome measures (PROMs)

Lu, S.-C., Xu, C., Kaur, M. N., Edelen, M. O., Pusic, A. L., & Sidey-Gibbons, C. J. (2025) · Journal of Patient-Reported Outcomes · doi:10.1186/s41687-025-00926-w · Cited by 13 · see citing works

HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints

Ramnath, S., Johnson, M., Gupta, A., & Raghuveer, A. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.129 · Cited by 9 · see citing works

Out of the BLEU: An Error Analysis of Statistical and Neural Machine Translation of WikiHow Articles from English into Arabic

Diab, N. (2021) · CDELT Occasional Papers in the Development of English Education · doi:10.21608/opde.2021.208437 · Cited by 8 · see citing works

Evaluating Multiway Multilingual NMT in the Turkic LanguagesPreprint

Mirzakhalov, J., Babu, A. J. G., Kunafin, A., Wahab, A., Moydinboyev, B., Ivanova, S., Uzokova, M., Pulatova, S., Ataman, D., Kreutzer, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06262 · Cited by 8 · see citing works

The use of residual analysis to improve the error rate accuracy of machine translation

Benko, Ľ., Munková, D., Munk, M., Benkova, L., & Hájek, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-59524-3 · Cited by 6 · see citing works

The Six Conundrums of Building and Deploying Language Technologies for Social Good

Diddee, H., Bali, K., Choudhury, M., & Mukhija, N. (2022) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3530190.3534792 · Cited by 7 · see citing works

Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements

von Werra, L., Tunstall, L., Thakur, A., Luccioni, A. S., Thrush, T., Piktus, A., Marty, F., Rajani, N. F., Mustar, V., & Ngo, H. (2022) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2022.emnlp-demos.13 · Cited by 7 · see citing works

Automatic Scoring for Translations Based on Language Models

Wu, D., Wang, M., & Li, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2171206 · Cited by 6 · see citing works

Experimental Standards for Deep Learning in Natural Language Processing Research

Ulmer, D., Bassignana, E., Müller-Eberstein, M., Varab, D., Zhang, M., van der Goot, R., Hardmeier, C., & Plank, B. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.196 · Cited by 6 · see citing works

Lessons from the Trenches on Reproducible Evaluation of Language Models

Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.14782 · Cited by 9 · see citing works

Morpheme-Based Neural Machine Translation Models for Low-Resource Fusion Languages

Gezmu, A. M., & Nürnberger, A. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3610773 · Cited by 5 · see citing works

Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science

Riezler, S., & Hagmann, M. (2021) · Synthesis lectures on human language technologies · doi:10.2200/s01137ed1v01y202110hlt055 · Cited by 5 · see citing works

It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance

Subramonian, A., Yuan, X., Daumé, H., & Blodgett, S. L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.202 · Cited by 7 · see citing works

Is NLP Ready for Standardization?

Aufrant, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.202 · Cited by 4 · see citing works

Post-editing of English to Russian machine translation: Issues, methods, and optimization

Khromova, A. A., & Lukmanova, R. R. (2024) · Philology Theory & Practice · doi:10.30853/phil20240138 · Cited by 4 · see citing works

Initial exploration into sarcasm and irony through machine translation

Chia, Z. L., Ptaszynski, M. E., Karpinska, M., Eronen, J., & Masui, F. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100106 · Cited by 7 · see citing works

EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics

Larionov, D., Grünwald, J., Leiter, C., & Eger, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.7 · Cited by 5 · see citing works

Did AI get more negative recently?

Beese, D., Altunbaş, B., Güzeler, G., & Eger, S. (2023) · Royal Society Open Science · doi:10.1098/rsos.221159 · Cited by 4 · see citing works

Bidirectional Translation of ASL and English Using Machine Vision and CNN and Transformer Networks

Amiruzzaman, S., Amiruzzaman, M., Batchu, R. M., Dracup, J., Pham, A., Crocker, B., Ngo, L. B., & Dewan, M. A. A. (2026) · Computers · doi:10.3390/computers15010020 · Cited by 3 · see citing works

When does Parameter-Efficient Transfer Learning Work for Machine Translation?

Üstün, A., & Stickland, A. C. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.540 · Cited by 4 · see citing works

Student textual feedback sentiment analysis using machine learning techniques to improve the quality of education

Ahmed, N., Khuhro, M. A., Khan, A., Dawood, M., Dootio, M. A., & Jan, N. U. (2023) · Pakistan Journal of Engineering Technology & Science · doi:10.22555/pjets.v11i2.1039 · Cited by 3 · see citing works

Few-shot Controllable Style Transfer for Low-Resource Settings: A Study in Indian Languages.Preprint

Krishna, K., Nathani, D., García, X., Samanta, B., & Talukdar, P. P. (2021) · arXiv (Cornell University) · Cited by 3 · see citing works

cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSEPreprint

Han, L., Sorokina, I., Erofeev, G., & Gladkoff, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.09484 · Cited by 3 · see citing works

Experimental Standards for Deep Learning in Natural Language Processing ResearchPreprint

Ulmer, D., Bassignana, E., Müller-Eberstein, M., Varab, D., Zhang, M., van der Goot, R., Hardmeier, C., & Plank, B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.06251 · Cited by 5 · see citing works

Quantifying Variance in Evaluation Benchmarks

Madaan, L., Singh, A. K., Schaeffer, R., Poulton, A., Koyejo, S., Stenetorp, P., Narang, S., & Hupkes, D. (2024) · arXiv · doi:10.48550/arxiv.2406.10229 · Cited by 2 · see citing works

Improving the Performance of Image Captioning Models Trained on Small Datasets

du Plessis, M., & Brink, W. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-95070-5_6 · Cited by 2 · see citing works

National Institute of Information and Communications Technology

Sumita, E., Ohtake, K., & Uchimoto, K. (2022) · Journal of Natural Language Processing · doi:10.5715/jnlp.29.748 · Cited by 2 · see citing works

Towards Automatic Evaluation of NLG Tasks Using Conversational Large Language Models

Riyadh, M., & Shafiq, M. O. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-34107-6_34 · Cited by 3 · see citing works

Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics

Xiang, J., Li, H., Liu, Y., Liu, L., Huang, G., Lian, D., & Shi, S. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.14 · Cited by 2 · see citing works

Guidance to Pre-tokeniztion for SacreBLEU: Meta-Evaluation in KoreanPreprint

Kim, A., & Kim, J. (2022) · Preprints.org · doi:10.20944/preprints202201.0018.v1 · Cited by 2 · see citing works

Human Emotion and Sentiment in Natural Language Understanding and Generation using Large Language Models with Limited to No Labeled Data

Riyadh, M. (2023) · doi:10.22215/etd/2023-15505 · Cited by 2 · see citing works

Significance

Riezler, S., & Hagmann, M. (2024) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-57065-0_4 · Cited by 1 · see citing works

Vacillating Human Correlation of SacreBLEU in Unprotected Languages

Kim, A., & Kim, J. (2022) · doi:10.18653/v1/2022.humeval-1.1 · Cited by 1 · see citing works

Introduction

Moniz, H., & Escartín, C. P. (2023) · Machine translation · doi:10.1007/978-3-031-14689-3_1 · Cited by 1 · see citing works

TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation

Ai, Y., He, Z., Yu, K., & Wang, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.164 · Cited by 1 · see citing works

A review of existing Machine Translation Approaches, their Challenges and Evaluation Metrics

Ahmed, N. (2023) · Pakistan Journal of Engineering Technology & Science · doi:10.22555/pjets.v11i1.1002 · Cited by 1 · see citing works

On Degrees of Freedom in Defining and Testing Natural Language Understanding

Sugawara, S., & Tsugita, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.861 · Cited by 1 · see citing works

Overview of NLPCC Shared Task 2: Multi-perspective Scientific Machine Reading Comprehension

Zhang, X., Zheng, H., Nie, Y., & Mao, X.-L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44699-3_10 · Cited by 1 · see citing works

Scientific Credibility of Machine Translation Research: A Meta-Evaluation of Evaluation Methodology

Marie, B., Fujita, A., & Rubino, R. (2021) · Journal of Natural Language Processing · doi:10.5715/jnlp.28.1312

Group Assignments for Project-Based Learning Using Natural Language Processing—A Feasibility Study

Kim, W., & Yoo, Y. (2022) · Applied Sciences · doi:10.3390/app12136321

Report on the 16th Symposiums of Young Researcher Association for NLP Studies

Takase, S. (2022) · Journal of Natural Language Processing · doi:10.5715/jnlp.29.1010

Interpreting Positional Information in Perspective of Word Order

Xilong, Z., Ruochen, L., Liu, J., & Xuefeng, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.534 · Cited by 1 · see citing works

Domain-adapting and evaluating machine translation for institutional German in South Tyrol

Contarino, A. G., & Camillis, F. D. (2023) · Studies in corpus linguistics · doi:10.1075/scl.113.10con

Learning Shortcuts: On the Misleading Promise of NLU in Language Models

Bihani, G., & Rayz, J. T. (2025) · Springer eBooks · doi:10.1007/978-3-031-67886-8_9 · Cited by 1 · see citing works

UEwMT: Leveraging User Experience and Deep Learning-Driven Methodology for Evaluating Machine Translation Services

Sharou, K. A., Jamei, M. K., Khalifa, I. B., Missaoui, S., Messaoud, M. B., & Moorkens, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-7423-7_10

RSDiX: Lightweight and Data-Efficient VLMs for Remote Sensing through Self-Distillation

Terlizzi, A., Nazzaro, A., Bernardi, L., Bardozzo, F., & Tagliaferri, R. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228553

U-DPO: A Unified Approach to Hallucination Reduction in LLM-Based Translation

Thai, N. V., Tien, N. B. N., & Dang, N. Q. (2025) · International Conference on Mechatronics Technology (ICMT) · doi:10.1109/icmt67823.2025.11298744

Neural Approaches to Historical Words Reconstruction

Fourrier, C. (2022) · theses.fr (ABES) · doi:10.70675/647b8a58zeea0z48cdz8229zf1d7ee8b0742 · Cited by 2 · see citing works

Writing in two languages : Neural machine translation as an assistive bilingual writing tool

Xu, J. (2022) · theses.fr (ABES) · doi:10.70675/f942ee6bzc6e6z47e4zbdbezea1b30beab75

Machine Translation of User-Generated Contents : an Evaluation of Neural Translation Systems under Zero-shot Conditions

Nuñez, J. R. (2023) · doi:10.70675/f8949afezdad2z4991z8aeez848d2abea5f5

Évaluation experte d'un prototype d'aide à la traduction créative : la traduction littéraire automatique individualisée au regard de ses enjeux traductologiques, éthiques et sociétaux

Hansen, D. (2024) · theses.fr (ABES) · doi:10.70675/409c7603z4a59z471bz85e5zba805a31f179

Visual Semantics in MT Evaluation: Do Image Descriptions Help with Assessment of Multimodal MT Quality?

Haq, S. U., Castilho, S., & Graham, Y. (2026) · Information · doi:10.3390/info17070689

The influence of socratic dialogue within GenAI on critical thinking and technology perception

Bereznikova, A., Theophilou, E., & Hernández‐Leo, D. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10693-0

The Benchmark Lottery

Dehghani, M., Tay, Y., Gritsenko, A. A., Zhao, Z., Houlsby, N., Diaz, F., Metzler, D., & Vinyals, O. (2021) · arXiv · doi:10.48550/arxiv.2107.07002 · Cited by 48 · see citing works

Scientific Credibility of Machine Translation Research: A Meta-Evaluation of Evaluation Methodology

Marie, B., Fujita, A., & Rubino, R. (2021) · Journal of Natural Language Processing · doi:10.5715/jnlp.28.1312

On the notion of validity and the evaluation of machine translation systems

King, M. (1996) · Benjamins translation library · doi:10.1075/btl.18.19kin

The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics

Rei, R., Guerreiro, N. M., Treviso, M., Coheur, L., Lavie, A., & Martins, A. F. T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.94 · Cited by 9 · see citing works

Machine Translation and Language Accuracy: A Comparative Analysis

(2026) · doi:10.59646/804/29

Evaluation Method of Machine Translation: from the Viewpoint of Natural Language Processing

Yokoyama, S. (1993) · Cited by 2 · see citing works