Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments

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

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Minimum error rate training in statistical machine translation

Och, F. J. (2003) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075096.1075117 · Cited by 2,728

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

Confidence estimation for machine translation

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Lattice-based minimum error rate training for statistical machine translation

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Precision and recall of machine translation

Melamed, I. D., Green, R., & Turian, J. P. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073483.1073504 · Cited by 228

The Significance of Recall in Automatic Metrics for MT Evaluation

Lavie, A., Sagae, K., & Jayaraman, S. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30194-3_16 · Cited by 123

CDER: Efficient MT Evaluation Using Block Movements.

Leusch, G., Ueffing, N., & Ney, H. (2006) · RWTH Publications (RWTH Aachen) · Cited by 109

Information Retrieval

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NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge

Vo, D. M., Chen, H., Sugimoto, A., & Nakayama, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01747 · Cited by 26

Harnessing the Power of Prompt-based Techniques for Generating School-Level Questions using Large Language Models

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NIST 2013 Open Handwriting Recognition and Translation (Open HaRT'13) Evaluation

Tong, A. N., Przybocki, M. A., Märgner, V., & Abed, H. E. (2014) · IAPR International Workshop on Document Analysis Systems (DAS) · doi:10.1109/das.2014.43 · Cited by 23

Neural Automatic Post-Editing Using Prior Alignment and Reranking

Pal, S., Naskar, S. K., Vela, M., Liu, Q., & van Genabith, J. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2056 · Cited by 23

Qualitative Analysis of Post-Editing for High Quality Machine Translation

Blain, F., Sénellart, J., Schwenk, H., Plitt, M., & Roturier, J. (2011) · Wolverhampton Intellectual Repository and E-Theses (University of Wolverhampton) · Cited by 23

Adaptive Spatial Location With Balanced Loss for Video Captioning

Li, L., Zhang, Y., Tang, S., Xie, L., Li, X., & Tian, Q. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3045735 · Cited by 23

Video Question-Answering Techniques, Benchmark Datasets and Evaluation Metrics Leveraging Video Captioning: A Comprehensive Survey

Khurana, K., & Deshpande, U. A. (2021) · IEEE Access · doi:10.1109/access.2021.3058248 · Cited by 26

Att-BiL-SL: Attention-Based Bi-LSTM and Sequential LSTM for Describing Video in the Textual Formation

Ahmed, S., Saif, A. F. M. S., Hanif, M. I., Shakil, M. M. N., Jaman, M. M., Mazid-Ul-Haque, M., Shawkat, S. B., Hasan, J., Sonok, B. S., Rahman, F. M. N., et al. (2021) · Applied Sciences · doi:10.3390/app12010317 · Cited by 24

A Survey on Neural Data-to-Text Generation

Lin, Y., Ruan, T., Liu, J., & Wang, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3304385 · Cited by 25

Deep Learning Based Image Aesthetic Quality Assessment- A Review

Chounchenani, M. D., Shahbahrami, A., Hassanpour, R., & Gaydadjiev, G. N. (2025) · ACM Computing Surveys · doi:10.1145/3716820 · Cited by 27

Spatio-Temporal and Retrieval-Augmented Modeling for Chest X-Ray Report Generation

Yang, Y. Q., You, X., Zhang, K., Fu, Z., Wang, X., Ding, J., Sun, J., Zhou, Y., Huang, Q., Han, W., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3554498 · Cited by 25

2010 Failures in English-Czech Phrase-Based MT

Bojar, O., & Kos, K. (2010) · Institute of Formal and Applied Linguistics (ÚFAL) · Cited by 22

Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors

Mahmud, J., Faisal, F., Arnob, R. I., Anastasopoulos, A., & Moran, K. (2021) · doi:10.18653/v1/2021.nlp4prog-1.1 · Cited by 27

Semi-Autoregressive Image Captioning

Yan, X., Fei, Z., Li, Z., Wang, S., Huang, Q., & Tian, Q. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475179 · Cited by 22

GRiT: A Generative Region-to-Text Transformer for Object Understanding

Wu, J., Wang, J., Yang, Z., Gan, Z., Liu, Z., Yuan, J., & Wang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72989-8_12 · Cited by 22

Regression for machine translation evaluation at the sentence level

Albrecht, J. S., & Hwa, R. (2008) · Machine Translation · doi:10.1007/s10590-008-9046-1 · Cited by 23

Semantic-filtered Soft-Split-Aware video captioning with audio-augmented feature

Xu, Y., Yang, J., & Mao, K. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.027 · Cited by 22

Unstructured Text Enhanced Open-Domain Dialogue System: A Systematic Survey

Ma, L., Li, M., Zhang, W., Li, J., & Liu, T. (2021) · ACM Transactions on Information Systems · doi:10.1145/3464377 · Cited by 24

Learning to Translate with Multiple Objectives

Duh, K., Sudoh, K., Wu, X., Tsukada, H., & Nagata, M. (2012) · Cited by 21

Multi-Turn Video Question Generation via Reinforced Multi-Choice Attention Network

Guo, Z., Zhao, Z., Jin, W., Wei, Z., Yang, M., Wang, N., & Yuan, N. J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3014775 · Cited by 21

Eye Tracking as a Tool for Machine Translation Error Analysis

Stymne, S., Danielsson, H., Bremin, S., Hu, H., Karlsson, J., Lillkull, A. P., & Wester, M. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3x28wwqt9mzw · Cited by 21

Generating Easy-to-Understand Referring Expressions for Target Identifications

Tanaka, M. N., Itamochi, T., Narioka, K., Sato, I., Ushiku, Y., & Harada, T. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00589 · Cited by 25

Revisiting Back-Translation for Low-Resource Machine Translation Between Chinese and Vietnamese

Li, H., Sha, J., & Shi, C. (2020) · IEEE Access · doi:10.1109/access.2020.3006129 · Cited by 21

Reordering metrics for statistical machine translation

Birch, A. (2011) · ERA · Cited by 21

Factored neural machine translation

Martínez, M. G. (2018) · theses.fr (ABES) · doi:10.70675/6b041d7cz8d47z4eb0zaefbz4170f95dfd99 · Cited by 21

Design and Performance Evaluation of LLM-Based RAG Pipelines for Chatbot Services in International Student Admissions

kizi, M. K. Z., & Suh, Y. (2025) · Electronics · doi:10.3390/electronics14153095 · Cited by 22

Generating Descriptions of Entity Relationships

Voskarides, N., Meij, E., & de Rijke, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56608-5_25 · Cited by 20

Posterior Control of Blackbox Generation

Li, X. L., & Rush, A. M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.243 · Cited by 20

PIE: A Parallel Idiomatic Expression Corpus for Idiomatic Sentence Generation and Paraphrasing

Zhou, J., Gong, H., & Bhat, S. (2021) · Workshop on Multiword Expressions (MWE) · doi:10.18653/v1/2021.mwe-1.5 · Cited by 20

Weakly Guided Hierarchical Encoder-Decoder Network for Brain CT Report Generation

Yang, S., Ji, J., Zhang, X., Liu, Y., & Wang, Z. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669626 · Cited by 20

Exploring sentence-level revision capabilities of large language models in English for academic purposes writing assistance

Du, Z., & Hashimoto, K. (2025) · Asian-Pacific Journal of Second and Foreign Language Education · doi:10.1186/s40862-025-00334-z · Cited by 20

Generation of Compound Words in Statistical Machine Translation into Compounding Languages

Stymne, S., Cancedda, N., & Ahrenberg, L. (2013) · Computational Linguistics · doi:10.1162/coli_a_00162 · Cited by 20

Evaluation of Free Online Machine Translations for Croatian-English and English-Croatian Language Pairs

Seljan, S., Bakarić, M. B., & Kučiš, V. (2011) · Repozitorij Filozofskog fakulteta u Zagrebu' at University of Zagreb (University of Zagreb) · Cited by 20

Why Catalan-Spanish Neural Machine Translation? Analysis, comparison and combination with standard Rule and Phrase-based technologies

Costa‐jussà, M. R. (2017) · Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial) · doi:10.18653/v1/w17-1207 · Cited by 20

Dual Learning for Visual Question Generation

Xu, X., Song, J., Lu, H., He, L., Yang, Y., & Shen, F. (2018) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486475 · Cited by 20

Improving Context Modelling in Multimodal Dialogue Generation

Agarwal, S., Dušek, O., Konstas, I., & Rieser, V. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6514 · Cited by 23

Neural Generation for Czech: Data and Baselines

Dušek, O., & Jurčíček, F. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8670 · Cited by 21

Adaptive Attention-based High-level Semantic Introduction for Image Caption

Liu, X., & Xu, Q. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3409388 · Cited by 20

Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions

Dua, R., Kancheti, S. S., & Balasubramanian, V. N. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00178 · Cited by 20

Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG

Juraska, J., & Walker, M. A. (2021) · International Conference on Natural Language Generation · doi:10.18653/v1/2021.inlg-1.45 · Cited by 20

Processing of Swedish Compounds for Phrase-Based Statistical Machine Translation

Stymne, S., & Holmqvist, M. (2008) · Cited by 20

Statistical Machine Translation

Specia, L. (2014) · IGI Global eBooks · doi:10.4018/978-1-4666-6042-7.ch043 · Cited by 20

Exploring Benefits of Transfer Learning in Neural Machine TranslationPreprint

Kocmi, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01622 · Cited by 23

Response Generation with Context-Aware Prompt LearningPreprint

Gu, X., Yoo, K. M., & Lee, S.-W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02643 · Cited by 26

Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

Zhou, L., & Huang, Y. (2021) · Future Internet · doi:10.3390/fi13020055 · Cited by 19

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Goldberg, A. B., Zhu, X., Dyer, C. R., Eldawy, M., & Heng, L. (2008) · Conference on Computational Natural Language Learning · doi:10.3115/1596324.1596345 · Cited by 19

Comparative Study Between METEOR and BLEU Methods of MT: Arabic into English Translation as a Case Study

Laith, S., Taghreed, M., & Mohammed, N. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.061128 · Cited by 19

Overview of the seventh Dialog System Technology Challenge: DSTC7

D’Haro, L. F., Yoshino, K., Hori, C., Marks, T. K., Polymenakos, L., Kummerfeld, J. K., Galley, M., & Gao, X. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101068 · Cited by 19

FFCI: A Framework for Interpretable Automatic Evaluation of Summarization

Koto, F., Baldwin, T., & Lau, J. H. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13167 · Cited by 25

Combining Confidence Estimation and Reference-based Metrics for Segment-level MT Evaluation

Specia, L., & Giménez, J. (2010) · Conference of the Association for Machine Translation in the Americas · Cited by 19

Machine Translation Evaluation and Optimization

Dorr, B. J., Olive, J. P., McCary, J., & Christianson, C. (2011) · Springer eBooks · doi:10.1007/978-1-4419-7713-7_5 · Cited by 19

Quda: Natural Language Queries for Visual Data AnalyticsPreprint

Fu, S., Xiong, K., Ge, X., Tang, S., Chen, W., & Wu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03257 · Cited by 28

Control Prefixes for Text Generation.Preprint

Clive, J., Cao, K., & Rei, M. (2021) · arXiv (Cornell University) · Cited by 19

Human and Automatic Evaluation of English to Hindi Machine Translation Systems

Joshi, N., Darbari, H., & Mathur, I. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-30157-5_42 · Cited by 18

RED: A Reference Dependency Based MT Evaluation Metric

Yu, H., Wu, X., Xie, J., Jiang, W., Liu, Q., & Lin, S. (2014) · Cited by 18

Significance tests of automatic machine translation evaluation metrics

Zhang, Y., & Vogel, S. (2010) · Machine Translation · doi:10.1007/s10590-010-9073-6 · Cited by 18

A Hybrid Rules and Statistical Method for Arabic to English Machine Translation

Alqudsi, A., Omar, N., & Shaker, K. (2019) · International Conference on Computer Applications & Information Security (ICCAIS) · doi:10.1109/cais.2019.8769545 · Cited by 19

An algorithm for cross-lingual sense-clustering tested in a MT evaluation setting

Apidianaki, M., & He, Y. (2010) · Arrow@dit (Dublin Institute of Technology) · Cited by 18

On the Generation of Medical Dialogues for COVID-19Preprint

Yang, W., Zeng, G., Tan, B., Ju, Z., Chakravorty, S., He, X., Chen, S., Yang, X., Wu, Q., Yu, Z., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05442 · Cited by 22

Adapter-Enhanced Hierarchical Cross-Modal Pre-Training for Lightweight Medical Report Generation

Yu, T., Lu, W., Yang, Y. Q., Han, W., Huang, Q., Yu, J., & Zhang, K. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3535699 · Cited by 18

CAT+: Investigating and Enhancing Audio-Visual Understanding in Large Language Models

Ye, Q., Yu, Z., Shao, R., Cui, Y., Kang, X., Liu, X., Torr, P. H. S., & Cao, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3582389 · Cited by 21

CharaParser+EQ: Performance evaluation without gold standard

Cui, H., Dahdul, W., Dececchi, A., Ibrahim, N., Mabee, P., Balhoff, J. P., & Gopalakrishnan, H. (2015) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2015.145052010020 · Cited by 17

#PraCegoVer: A Large Dataset for Image Captioning in Portuguese

dos Santos, G. O., Colombini, E. L., & Avila, S. (2022) · Data · doi:10.3390/data7020013 · Cited by 18

A Multimodal Framework for Video Caption Generation

Bhooshan, R. S., & Suresh, K. (2022) · IEEE Access · doi:10.1109/access.2022.3202526 · Cited by 18

Leveraging Pre-trained BERT for Audio Captioning

Liu, X., Mei, X., Huang, Q., Sun, J., Zhao, J., Liu, H., Plumbley, M. D., Kılıç, V., & Wang, W. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909761 · Cited by 22

Cascade Semantic Prompt Alignment Network for Image Captioning

Li, J., Zhang, L., Zhang, K., Hu, B., Xie, H., & Mao, Z. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3343520 · Cited by 18

Paraphrase Generation and Supervised Learning for Improved Automatic Short Answer Grading

Ouahrani, L., & Bennouar, D. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00391-w · Cited by 19

Boosting court judgment prediction and explanation using legal entities

Benedetto, I., Koudounas, A., Vaiani, L., Pastor, E., Cagliero, L., Tarasconi, F., & Baralis, E. (2024) · Artificial Intelligence and Law · doi:10.1007/s10506-024-09397-8 · Cited by 17

Exploring Progress in Text-to-Image Synthesis: An In-Depth Survey on the Evolution of Generative Adversarial Networks

Habib, M. A., Wadud, M. A. H., Patwary, M. F. K., Rahman, M. M., Mridha, M. F., Okuyama, Y., & Shin, J. (2024) · IEEE Access · doi:10.1109/access.2024.3435541 · Cited by 19

LAYERED: Metric for Machine Translation Evaluation

Gautam, S., & Bhattacharyya, P. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3350 · Cited by 17

Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation

Ruiter, D., van Genabith, J., & España-Bonet, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.202 · Cited by 17

Automatic image and text-based description for colorectal polyps using BASIC classification

Fonollà, R., van der Zander, Q. E. W., Schreuder, R. M., Subramaniam, S., Bhandari, P., Masclee, A., Schoon, E. J., van der Sommen, F., & de With, P. H. N. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102178 · Cited by 18

Multiple Communication Channels in Literary Texts

Matricciani, E. (2022) · Open Journal of Statistics · doi:10.4236/ojs.2022.124030 · Cited by 17

Capacity of Linguistic Communication Channels in Literary Texts: Application to Charles Dickens’ Novels

Matricciani, E. (2023) · Information · doi:10.3390/info14020068 · Cited by 17

Analytic Quality

Zahálka, J., Rudinac, S., & Worring, M. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806279 · Cited by 17

Integrating source-language context into phrase-based statistical machine translation

Haque, R., Naskar, S. K., van den Bosch, A. P. J., & Way, A. (2011) · Machine Translation · doi:10.1007/s10590-011-9100-2 · Cited by 17

Machine Translation for Human Translators

Denkowski, M. (2022) · doi:10.1184/r1/21671609 · Cited by 17

Pivot Machine Translation Using Chinese as Pivot Language

Liu, C.-H., Silva, C. C., Wang, L., & Way, A. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-3083-4_7 · Cited by 17

Decoding with syntactic and non-syntactic phrases in a syntax-based machine translation system

Hanneman, G., & Lavie, A. (2009) · Workshop on Syntax and Structure in Statistical Translation (SSST) · doi:10.3115/1626344.1626345 · Cited by 17

Morphological processing of compounds for statistical machine translation

Cap, F. (2014) · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · doi:10.18419/opus-3474 · Cited by 17

The statistical advantage of automatic NLG metrics at the system level

Wei, J., & Jia, R. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.533 · Cited by 16

Schema-Guided Natural Language Generation

Du, Y., Oraby, S., Perera, V., Shen, M., Narayan-Chen, A., Chung, T., Venkatesh, A., & Hakkani-Tür, D. Z. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.35 · Cited by 17

Weakly guided attention model with hierarchical interaction for brain CT report generation

Zhang, X., Yang, S., Shi, Y., Ji, J., Liu, Y., Wang, Z., & Xu, H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107650 · Cited by 16

Opinerium: Subjective Question Generation Using Large Language Models

Babakhani, P., Lommatzsch, A., Brodt, T., Sacker, D., Sivrikaya, F., & Albayrak, Ş. (2024) · IEEE Access · doi:10.1109/access.2024.3398553 · Cited by 17

Multi-Modal Instruction Tuned LLMs with Fine-Grained Visual Perception

He, J., Wang, Y., Wang, L., Lu, H., He, J.-Y., Lan, J.-P., Luo, B., & Xie, X. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01326 · Cited by 26

Machine Translation Evaluation: Unveiling the Role of Dense Sentence Vector Embedding for Morphologically Rich Language

Tripathi, S., & Kansal, V. (2019) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001420590016 · Cited by 16

Better than Average: Paired Evaluation of NLP systems

Peyrard, M., Zhao, W., Eger, S., & West, R. (2021) · doi:10.18653/v1/2021.acl-long.179 · Cited by 27

Video Description: Datasets & Evaluation Metrics

Rafiq, M. T., Rafiq, G., & Choi, G. S. (2021) · IEEE Access · doi:10.1109/access.2021.3108565 · Cited by 16

A conjoint analysis framework for evaluating user preferences in machine translation

Kirchhoff, K., Daniel Capurro, N., & Turner, A. M. (2013) · Machine Translation · doi:10.1007/s10590-013-9140-x · Cited by 16

The circle of meaning: from translation to paraphrasing and back

Dorr, B. J., & Madnani, N. (2010) · University Libraries (University of Maryland) · Cited by 16

Text2Scene: Generating Abstract Scenes from Textual Descriptions.Preprint

Tan, F., Feng, S., & Ordóñez, V. (2018) · arXiv (Cornell University) · Cited by 16

Template Guided Text Generation for Task-Oriented DialoguePreprint

Kale, M., & Rastogi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.15006 · Cited by 26

Are metrics measuring what they should? An evaluation of Image Captioning task metrics

González-Chávez, O., Ruíz, G., Moctezuma, D., & Ramírez-delReal, T. A. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.117071 · Cited by 15

End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric

Raza, H., & Shahzad, W. (2024) · IEEE Access · doi:10.1109/access.2024.3377463 · Cited by 17

Taming Data and Transformers for Audio Generation

Haji-Ali, M., Menapace, W., Siarohin, A., Balakrishnan, G., & Ordóñez, V. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02632-y · Cited by 31

Learning to Translate: A Statistical and Computational Analysis

Turchi, M., Bie, T. D., Goutte, C., & Cristianini, N. (2012) · Advances in Artificial Intelligence · doi:10.1155/2012/484580 · Cited by 15

Referential translation machines for predicting semantic similarity

Biçici, E., & Way, A. (2015) · Language Resources and Evaluation · doi:10.1007/s10579-015-9322-7 · Cited by 15

Neural Machine Translation by Generating Multiple Linguistic Factors

García-Martínez, M., Barrault, L., & Bougares, F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68456-7_2 · Cited by 15

Improving LEO Robot Conversational Ability via Deep Learning Algorithms for Children with Autism

She, T., Kang, X., Nishide, S., & Ren, F. (2018) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2018.8691393 · Cited by 15

Adaptive Syncretic Attention for Constrained Image Captioning

Yang, L., & Hu, H. (2019) · Neural Processing Letters · doi:10.1007/s11063-019-10045-5 · Cited by 15

METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments

Lavie, A., & Agarwal, A. (2005)

The Meteor metric for automatic evaluation of machine translation

Lavie, A., & Denkowski, M. (2009) · Machine Translation · doi:10.1007/s10590-009-9059-4 · Cited by 351

Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems

Denkowski, M., & Lavie, A. (2011) · Cited by 344

Meteor, m-bleu and m-ter: Evaluation Metrics for High-Correlation with Human Rankings of Machine Translation Output

Agarwal, A., & Lavie, A. (2008)

METEOR, M-BLEU and M-TER

Agarwal, A., & Lavie, A. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626406 · Cited by 109