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

Untitled work

(2005) · Cited by 4,152

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

Blatz, J., Fitzgerald, E., Foster, G. F., Gandrabur, S., Goutte, C., Kulesza, A., Sanchís, A., & Ueffing, N. (2004) · International Conference on Computational Linguistics · doi:10.3115/1220355.1220401 · Cited by 368

Lattice-based minimum error rate training for statistical machine translation

Macherey, W., Och, F. J., Thayer, I., & Uszkoreit, J. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613807 · Cited by 347

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

(2024) · ACM eBooks · doi:10.1145/3674127 · Cited by 86

Exploring the Spatio‐Temporal Aware Graph for video captioning

Xue, P., & Zhou, B. (2022) · IET Computer Vision · doi:10.1049/cvi2.12103 · Cited by 6

Techniques, datasets, evaluation metrics and future directions of a question answering system

Qamar, F., Latif, S., & Shah, A. A. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02019-w · Cited by 6

End-to-End Video Captioning Based on Multiview Semantic Alignment for Human–Machine Fusion

Wu, S., Gao, Y., Yang, W., Li, H., & Zhu, G. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3352903 · Cited by 6

Improved Urdu-English Neural Machine Translation with a fully Convolutional Neural Network Encoder

Israr, H., Shahzad, M. K., & Anwar, S. (2024) · International Journal of Mathematical Engineering and Management Sciences · doi:10.33889/ijmems.2024.9.5.056 · Cited by 6

LLMRAG: An Optimized Digital Support Service using LLM and Retrieval-Augmented Generation

Cederlund, O., Alawadi, S., & Awaysheh, F. M. (2024) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec62297.2024.10710181 · Cited by 6

Traitement automatique des entités nommées en arabe : détection et traduction

Gahbiche, S., Bonneau-Maynard, H., & Yvon, F. (2014) · HAL · Cited by 6

Language Model Augmented Relevance Score

Liu, R., Wei, J., & Vosoughi, S. (2021) · doi:10.18653/v1/2021.acl-long.521 · Cited by 10

Multilingual sequence to sequence convolutional machine translation

Bansal, M., & Lobiyal, D. K. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11345-6 · Cited by 6

Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model

Zhu, L., Xu, Y., Zhu, Z., Bao, Y., & Kong, X. (2022) · Applied Sciences · doi:10.3390/app13010264 · Cited by 6

Image captioning to aid blind and visually impaired outdoor navigation

Faurina, R., Jelita, A., Vatresia, A., & Agustian, I. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i3.pp1104-1117 · Cited by 6

TCNAEC: Advancing Sentence-Level Revision Evaluation Through Diverse Non-Native Academic English Insights

Du, Z., & Hashimoto, K. (2023) · IEEE Access · doi:10.1109/access.2023.3342862 · Cited by 6

Automatic Metrics for Machine Translation Evaluation and Minority Languages

Munková, D., & Munk, M. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-30298-0_69 · Cited by 6

Textual Entailment Using Machine Translation Evaluation Metrics

Saikh, T., Naskar, S. K., Ekbal, A., & Bandyopadhyay, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77113-7_25 · Cited by 6

Towards Machine Translation for the Kurdish Language

Ahmadi, S., & Masoud, M. (2020) · doi:10.18653/v1/2020.loresmt-1.12 · Cited by 6

Incorporating Translation Quality Estimation into Chinese-Korean Neural Machine Translation

Li, F., Zhao, Y., Yang, F., & Cui, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84186-7_4 · Cited by 6

ExpScore: Learning Metrics for Recommendation Explanation

Wen, B., Feng, Y., Zhang, Y., & Shah, C. S. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512269 · Cited by 6

What Makes a Good Podcast Summary?

Rezapour, R., Reddy, S., Jones, R., & Soboroff, I. M. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531802 · Cited by 8

LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing

Li, Y., Arnold, J., Yan, F., Shi, W., & Yu, Z. (2021) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2021.acl-demo.38 · Cited by 6

A SomAgent statistical machine translation

López, V. F., Corchado, J. M., Paz, J. F. D., Rodrı́guez, S., & Bajo, J. (2010) · Applied Soft Computing · doi:10.1016/j.asoc.2010.08.018 · Cited by 6

Domain Adaptation for Statistical Machine Translation of Corporate and User-Generated Content

Banerjee, P. (2013) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 6

Searching for Ambiguous Objects in Videos using Relational Referring ExpressionsPreprint

Anayurt, H., Ozyegin, S. A., Cetin, U., Aktas, U., & Kalkan, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01189 · Cited by 6

How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation MetricsPreprint

Parthasarathi, P., Pineau, J., & Chandar, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.10427 · Cited by 6

XAI-based Data Visualization in Multimodal Medical DataPreprint

Sharma, S., Singh, M., Mcdaid, L. J., & Bhattacharyya, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.11.664302 · Cited by 7

Evaluating Medical Text Summaries Using Automatic Evaluation Metrics and LLM-as-a-Judge Approach: A Pilot Study

Vasilev, Y., Raznitsyna, I., Pamova, A., Burtsev, T., Bobrovskaya, T., Kosov, P., Vladzymyrskyy, A., Omelyanskaya, O., & Arzamasov, K. (2025) · Diagnostics · doi:10.3390/diagnostics16010003 · Cited by 10

Lext: Towards evaluating trustworthiness of natural language explanations

Shailya, K., Rajpal, S., Krishnan, G. S., & Ravindran, B. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732104 · Cited by 5

Exploring Brazil's LLM Fauna: Investigating the Generative Performance of Large Language Models in Portuguese

Assis, G., Freitas, C., & Paes, A. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5814 · Cited by 5

Leveraging Large Language Models for Tacit Knowledge Discovery in Organizational Contexts

Zuin, G. L., Mastelini, S. M., Loures, T. C., & Veloso, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227259 · Cited by 5

Multi-channel weighted fusion for image captioning

Zhong, J., Cao, Y., Zhu, Y., Gong, J., & Chen, Q. (2022) · The Visual Computer · doi:10.1007/s00371-022-02716-7 · Cited by 5

Video captioning using transformer-based GAN

Babavalian, M. R., & Kiani, K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19247-z · Cited by 5

Knowledge-Aligned Domain Shift Tuning for Efficient Adaptation in Large Language Models

Kawamae, N. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737013 · Cited by 5

Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos

Stamatakis, M., Berger, J., Wartena, C., Ewerth, R., & Hoppe, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98417-4_22 · Cited by 6

Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts

Groves, I., Tian, Y., & Douratsos, I. (2018) · International Conference on Natural Language Generation · doi:10.18653/v1/w18-6512 · Cited by 5

Investigating post-editing effort

Stasimioti, M., Sosoni, V., & Chatzitheodorou, K. (2021) · Cognitive Linguistic Studies · doi:10.1075/cogls.00083.sta · Cited by 5

Stories of love and violence: zero-shot interesting events’ classification for unsupervised TV series summarization

Reboud, A., Harrando, I., Lisena, P., & Raphaël Troncy, A. (2023) · Multimedia Systems · doi:10.1007/s00530-022-01040-3 · Cited by 5

PWS-DVC: Enhancing Weakly Supervised Dense Video Captioning With Pretraining Approach

Choi, W., Chen, J., & Yoon, J. (2023) · IEEE Access · doi:10.1109/access.2023.3331756 · Cited by 5

Math Word Problem Generation via Disentangled Memory Retrieval

Qin, W., Wang, X., Hu, Z., Wang, L., Lan, Y., & Hong, R. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3639569 · Cited by 5

HIPPL: Hierarchical Intent-Inferring Pointer Network With Pseudo Labeling for Consistent Persona-Driven Dialogue Generation [Research Frontier]

Zhu, L., Li, W., Mao, R., & Cambria, E. (2024) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2024.3446133 · Cited by 5

Lightly supervised word-sense translation-error detection and resolution in an interactive conversational spoken language translation system

Ananthakrishnan, S., Mehay, D. N., Hewavitharana, S., Kumar, R., Roy, M., & Kan, E. (2015) · Machine Translation · doi:10.1007/s10590-015-9168-1 · Cited by 5

A Stylometric Study and Assessment of Machine Translators

Suresh, V., Krishnamurthy, A., Badrinath, R., & Madhavan, C. E. V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24800-9_34 · Cited by 5

Pseudo-Word for Phrase-Based Machine Translation

Duan, X., Zhang, M., & Li, H. (2010) · Cited by 5

Integration of Multiple Bilingually-Trained Segmentation Schemes into Statistical Machine Translation

Paul, M. D., FINCH, A., & Sumita, E. (2011) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e94.d.690 · Cited by 5

Statistical machine translation and automatic speech recognition under uncertainty

Byrne, B., & Mathias, L. (2008) · Cited by 5

WIDAR - Weighted Input Document Augmented ROUGE

Jain, R., Mavi, V., Jangra, A., & Saha, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99736-6_21 · Cited by 6

MSVD-Turkish: A Large-Scale Dataset for Video Captioning in Turkish

Çtamak, B., Kuyu, M., Erdem, A., & Erdem, E. (2019) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2019.8806555 · Cited by 5

Grounded Sequence to Sequence Transduction

Specia, L., Barrault, L., Çağlayan, O., Duarte, A., Elliott, D., Gella, S., Holzenberger, N., Lala, C., Lee, S. J., Libovický, J., et al. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2998415 · Cited by 5

Supporting search engines with knowledge and context

Voskarides, N. (2021) · ACM SIGIR Forum · doi:10.1145/3527546.3527573 · Cited by 5

Improved Features and Grammar Selection for Syntax-Based MT

Hanneman, G., Clark, J. H., & Lavie, A. (2010) · Cited by 5

Bringing back simplicity and lightliness into neural image captioningPreprint

Delbrouck, J.-B., & Dupont, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06245 · Cited by 5

A Benchmark Dataset for Understandable Medical Language Translation.Preprint

Luo, J., Zheng, Z., Ye, H., Ye, M., Wang, Y., You, Q., Xiao, C., & Ma, F. (2020) · arXiv (Cornell University) · Cited by 5

Narrative Incoherence DetectionPreprint

Cai, D., Zhang, Y., Huang, Y., Lam, W. P., & Dolan, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11157 · Cited by 6

Multimodal Machine Translation

Çağlayan, O. (2019) · theses.fr (ABES) · doi:10.70675/b8273cd5z143bz49afz86dazfbcacd510c0b · Cited by 5

Learning to Generate Popular Headlines

Omidvar, A., & An, A. (2023) · IEEE Access · doi:10.1109/access.2023.3286853 · Cited by 4

Dynamic Debiasing Network for Visual Commonsense Generation

Kim, J., Park, J., Seok, J., & Kim, J. (2023) · IEEE Access · doi:10.1109/access.2023.3340705 · Cited by 4

Knowledge Graph-Enhanced Triplet Semantic Communication System for Low-Altitude IoT Transmission

Zhang, Z., Wang, S., Zhou, Q., Ma, L., Wen, R., & Mu, J. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3596170 · Cited by 4

BEnchmarking Large Language Models for Ophthalmology (BELO): An Expert-Curated Data Set and Evaluation Framework for Knowledge and Reasoning

Srinivasan, S., Ai, X., Lo, T. W. S., Gilson, A., Zou, M., Zou, K., Kim, H., Yang, M., Pushpanathan, K., Yew, S. M. E., et al. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.101050 · Cited by 4

Incorporating Multilingual Knowledge Distillation into Machine Translation Evaluation

Zhang, M., Yang, H., Tao, S., Zhao, Y., Qiao, X., Li, Y., Su, C., Wang, M., Guo, J., Liu, Y., et al. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-7596-7_12 · Cited by 4

Target-Side Language Model for Reference-Free Machine Translation Evaluation

Zhang, M., Qiao, X., Yang, H., Tao, S., Zhao, Y., Li, Y., Su, C., Wang, M., Guo, J., Liu, Y., et al. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-7960-6_5 · Cited by 4

Using various pre-trained models for audio feature extraction in automated audio captioning

Won, H., Kim, B., Kwak, I., & Lim, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120664 · Cited by 5

Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge

Zhang, Y., Wang, M., & Yu, J. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00094 · Cited by 4

Enhancing Interactive Storytelling: A Computational Approach to Autonomously Generating Role-Playing Game Scripts with Natural Language Processing

Zhong, Q. (2023) · International Signal Processing, Communications and Engineering Management Conference (ISPCEM) · doi:10.1109/ispcem60569.2023.00134 · Cited by 7

RetrievalMMT: Retrieval-Constrained Multi-Modal Prompt Learning for Multi-Modal Machine Translation

Wang, Y., YaWen, Z., Liang, J., Xing, X., Xu, J., & Xu, X. (2024) · International Conference on Multimedia Retrieval · doi:10.1145/3652583.3658018 · Cited by 4

Question‐driven text summarization using an extractive‐abstractive framework

Kia, M. A., Garifullina, A., Kern, M., Chamberlain, J., & Jameel, S. (2024) · Computational Intelligence · doi:10.1111/coin.12689 · Cited by 4

Towards Vietnamese Question and Answer Generation: An Empirical Study

Hùng, P. Q., Le, H.-L., Nhat, M. D., Khang Tran, T., Tran-Tien, M., Dang, V.-H., Vu, H.-T., Nguyen, M.-T., & Phan, X.-H. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3675781 · Cited by 4

The Universitat d'Alacant hybrid machine translation system for WMT 2011

Sánchez-Cartagena, V. M., Sánchez-Martínez, F., & Pérez-Ortiz, J. A. (2011) · RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) · Cited by 4

Improving Evaluation of English-Czech MT through Paraphrasing

Baranċíková, P., Rosa, R., & Tamchyna, A. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4ujp9fuf8xq9 · Cited by 4

Getting Past the Language Gap: Innovations in Machine Translation

Delmonte, R. (2012) · Springer eBooks · doi:10.1007/978-1-4614-6018-3_6 · Cited by 4

JU_NLP@DPIL-FIRE2016: Paraphrase Detection in Indian Languages - A Machine Learning Approach.

Saikh, T., Naskar, S. K., & Bandyopadhyay, S. (2016) · FIRE (Working Notes) · Cited by 4

Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts

Bhowmik, R., & de Melo, G. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313656 · Cited by 4

Joint WMT 2013 Submission of the QUAERO Project

Peitz, S., Mansour, S., Huck, M., Freitag, M., Ney, H., Cho, E., Herrmann, T., Mediani, M., Niehues, J., Waibel, A., et al. (2013) · RWTH Publications (RWTH Aachen) · Cited by 4

Semantic textual similarity based on deep learning

Ranasinghe, T., Mitkov, R., Orǎsan, C., & Quintana, R. C. (2021) · Benjamins translation library · doi:10.1075/btl.158.04ran · Cited by 4

Automated generation of privacy policy using deep models

Bateni, N., & Dara, R. A. (2021) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas52410.2021.9629155 · Cited by 5

The Use of Machine Translation to Provide Resources for Under-Resourced Languages - Image Captioning Task

Ahmed, B. H. A., & Saad, M. (2021) · Palestinian International Conference on Information and Communication Technology (PICICT) · doi:10.1109/picict53635.2021.00017 · Cited by 4

One Type Context Is Not Enough: Global Context-aware Neural Machine Translation

Chen, L., Li, J., Gong, Z., Zhang, M., & Zhou, G. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3526215 · Cited by 4

NMT for English-Marathi using RNNs & Attention

Naik, A., Karani, M., Chheda, K., & Gada, M. (2022) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea54992.2022.10010782 · Cited by 4

FluGCF: A Fluent Dialogue Generation Model With Coherent Concept Entity Flow

Zhao, Y., Cheng, B. H., Huang, Y., & Wan, Z. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3340610 · Cited by 4

Visual Topic Semantic Enhanced Machine Translation for Multi-Modal Data Efficiency

Wang, C., Cai, S., Shi, B.-X., & Chong, Z. (2023) · Journal of Computer Science and Technology · doi:10.1007/s11390-023-1302-6 · Cited by 4

Image captions: global-local and joint signals attention model (GL-JSAM)

Naqvi, N., & Ye, Z. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09128-6 · Cited by 4

Attention-gated LSTM for Image Captioning

Xu, C., Ji, J., Zhang, M., & Zhang, X. (2019) · IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI) · doi:10.1109/icusai47366.2019.9124779 · Cited by 4

Topic-Aware Dialogue Generation with Two-Hop Based Graph Attention

Zhou, S., Rong, W., Zhang, J., Wang, Y., Shi, L., & Xiong, Z. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414472 · Cited by 4

Summary-Oriented Question Generation for Informational Queries

Yin, X., Zhou, L., Small, K., & May, J. (2021) · doi:10.18653/v1/2021.dialdoc-1.11 · Cited by 4

Human Action Captioning based on a GRU+LSTM+Attention Model

Zhou, L. M., Zhang, W., & Qian, X. (2021) · International Conference on Information Technology: IoT and Smart City (ICIT) · doi:10.1145/3512576.3512606 · Cited by 4

Overview of the NLPCC 2022 Shared Task: Multi-modal Dialogue Understanding and Generation

Wang, Y., Zhao, X., & Zhao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17189-5_29 · Cited by 4

Enhancing Self-disclosure In Open-Domain Dialogue By Candidate Re-ranking

Soni, M., Cowan, B. R., & Wade, V. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5538-9_17 · Cited by 5

Automated Image Captioning with Multi-layer Gated Recurrent Unit

MORAL, Ö. T., Kılıç, V., Onan, A., & Wang, W. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909888 · Cited by 4

Spectral Representation Learning and Fusion for Autonomous Vehicles Trip Description Exploiting Recurrent Transformer

Rafiq, G., Rafiq, M. T., & Choi, G. S. (2023) · IEEE Access · doi:10.1109/access.2023.3287783 · Cited by 4

Knowledge Distillation for Efficient Audio-Visual Video Captioning

ÇAYLI, Ö., Liu, X., Kılıç, V., & Wang, W. (2023) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco58844.2023.10290046 · Cited by 4

Methods for human evaluation of machine translation

Bremin, S., Hu, H., Karlsson, J., Lillkull, A. P., Wester, M., Danielsson, H., & Stymne, S. (2010) · Language and Technology Conference · Cited by 4

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Turchi, M., & Ehrmann, M. (2011) · Polibits · doi:10.17562/pb-43-5 · Cited by 4

MaxSim: performance and effects of translation fluency

Chan, Y. S., & Ng, H. T. (2009) · Machine Translation · doi:10.1007/s10590-009-9058-5 · Cited by 4

EXTRA: a system for example-based translation assistance

Mandreoli, F., Martoglia, R., & Tiberio, P. (2007) · Machine Translation · doi:10.1007/s10590-007-9023-0 · Cited by 4

Quality estimation-guided supplementary data selection for domain adaptation of statistical machine translation

Banerjee, P., Rubino, R., Roturier, J., & van Genabith, J. (2014) · Machine Translation · doi:10.1007/s10590-014-9165-9 · Cited by 4

Matura Evaluation experiment based on human evaluation of machine translation

Wojak, A., & Graliński, F. (2010) · Proceedings of the International Multiconference on Computer Science and Information Technology · doi:10.1109/imcsit.2010.5679864 · Cited by 4

Active Learning-Based Elicitation for Semi-Supervised Word Alignment

Ambati, V., Vogel, S., & Carbonell, J. (2010) · Cited by 4

Modeling Monolingual Character Alignment for Automatic Evaluation of Chinese Translation

Li, M., Wang, M., Li, H., & Xu, F. (2016) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/2815619 · Cited by 4

Continuous Language Generative Flow

Tang, Z., Zhang, S., Kim, H., & Bansal, M. (2021) · doi:10.18653/v1/2021.acl-long.355 · Cited by 4

Active Semi-Supervised Learning for Improving Word Alignment

Ambati, V., Vogel, S., & Carbonell, J. (2018) · doi:10.1184/r1/6620918 · Cited by 4

Sometimes We Want TranslationesePreprint

Parthasarathi, P., Sinha, K., Pineau, J., & Williams, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07623 · Cited by 4

An assistive framework for visually impaired using deep learning and natural language processing

Maheswari, V. U., Aluvalu, R., Dhanaraj, R. K., Al-Khasawneh, M. A., Manoharan, H., & Selvarajan, S. (2026) · International Journal of Speech Technology · doi:10.1007/s10772-026-10281-w · Cited by 3

TG-CUP: A Transformer and GNN-Based Multi-Modal Comment Updating Method

Chen, Y., Huang, Y., Chen, X., & Zheng, Z. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708474 · Cited by 3

Generating Abstractive Summaries from Social Media Discussions Using Transformers

Papagiannopoulou, A., Angeli, C., & Ahmad, M. B. (2025) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2025.151016 · Cited by 3

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