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

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Li, X. L., & Liang, P. (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.353 · Cited by 2,473

Generation and Comprehension of Unambiguous Object Descriptions

Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A., & Murphy, K. P. (2016) · 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/cvpr.2016.9 · Cited by 1,281

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.30 · Cited by 1,076

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776

Variational Autoencoder for Deep Learning of Images, Labels and CaptionsPreprint

Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A., & Carin, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.08976 · Cited by 669

SummEval: Re-evaluating Summarization Evaluation

Fabbri, A. R., Kryściński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00373 · Cited by 587

ECO: Efficient Convolutional Network for Online Video Understanding

Zolfaghari, M., Singh, K., & Brox, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_43 · Cited by 541

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C. M., & Eger, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1053 · Cited by 490

Guiding the Long-Short Term Memory Model for Image Caption Generation

Jia, X., Gavves, E., Fernando, B., & Tuytelaars, T. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.277 · Cited by 480

Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question AnsweringPreprint

Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., & Xu, W. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05612 · Cited by 441

Why We Need New Evaluation Metrics for NLG

Novikova, J., Dušek, O., Cercas Curry, A., & Rieser, V. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1238 · Cited by 422

Improving automatic source code summarization via deep reinforcement learning

Wan, Y., Zhao, Z., Yang, M., Xu, G., Ying, H., Wu, J., & Yu, P. S. (2018) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3238147.3238206 · Cited by 404

nocaps: novel object captioning at scale

Agrawal, H., Desai, K., Wang, Y., Chen, X., Jain, R., Johnson, M., Batra, D., Parikh, D., Lee, S., & Anderson, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00904 · Cited by 419

(Meta-) evaluation of machine translation

Callison-Burch, C., Fordyce, C. S., Koehn, P., Monz, C., & Schroeder, J. (2007) · Conference on Machine Translation (WMT) · doi:10.3115/1626355.1626373 · Cited by 381

In Defense of Grid Features for Visual Question Answering

Jiang, H., Misra, I., Rohrbach, M., Miller, E. G., & Chen, X. (2020) · 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/cvpr42600.2020.01028 · Cited by 384

Untitled work

(2020) · doi:10.18653/v1/2020.acl-main.450 · Cited by 356

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

Findings of the 2015 Workshop on Statistical Machine Translation

Bojar, O., Chatterjee, R., Federmann, C., Haddow, B., Huck, M., Hokamp, C., Koehn, P., Logacheva, V., Monz, C., Negri, M., et al. (2015) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w15-3001 · Cited by 287

The E2E Dataset: New Challenges For End-to-End Generation

Novikova, J., Dušek, O., & Rieser, V. (2017) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w17-5525 · Cited by 275

How Much Can CLIP Benefit Vision-and-Language Tasks?Preprint

Shen, S., Li, L. H., Tan, H. X., Bansal, M., Rohrbach, A., Chang, K.-W., Yao, Z., & Keutzer, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.06383 · Cited by 382

DialoGPT: Large-Scale Generative Pre-training for Conversational Response GenerationPreprint

Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00536 · Cited by 258

Neural Paraphrase Generation with Stacked Residual LSTM NetworksPreprint

Prakash, A., Hasan, S. A., Lee, K., Datla, V. V., Qadir, A., Liu, J., & Farri, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.03098 · Cited by 257

Adversarial text-to-image synthesis: A review

Frolov, S., Hinz, T., Raue, F., Hees, J., & Dengel, A. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.019 · Cited by 228

Paraphrase Generation with Deep Reinforcement Learning

Li, Z., Jiang, X., Shang, L., & Li, H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1421 · Cited by 229

Evaluation of Text Generation: A Survey

Celikyilmaz, A., Clark, E., & Gao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.14799 · Cited by 207

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204

Evaluating progress in automatic chest X-ray radiology report generation

Yu, F., Endo, M., Krishnan, R., Pan, I., Tsai, A., Reis, E. P., Fonseca, E. K. U. N., Lee, H. M. H., Abad, Z. S. H., Ng, A. Y., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100802 · Cited by 200

Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

Pagnoni, A., Balachandran, V., & Tsvetkov, Y. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.383 · Cited by 197

A Deep Generative Framework for Paraphrase Generation

Gupta, A., Agarwal, A. K., Singh, P., & Rai, P. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11956 · Cited by 197

Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge

Dušek, O., Novikova, J., & Rieser, V. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.06.009 · Cited by 209

Explainable Deep Learning Methods in Medical Image Classification: A Survey

Patrício, C., Neves, J. C., & Teixeira, L. F. (2023) · ACM Computing Surveys · doi:10.1145/3625287 · Cited by 207

Remote Sensing Image Change Captioning With Dual-Branch Transformers: A New Method and a Large Scale Dataset

Liu, C., Zhao, R., Chen, H., Zou, Z., & Shi, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3218921 · Cited by 203

Know More Say Less: Image Captioning Based on Scene Graphs

Li, X., & Jiang, S. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2896516 · Cited by 181

MedDialog: Large-scale Medical Dialogue Datasets

Zeng, G., Yang, W., Ju, Z., Yang, Y., Wang, S., Zhang, R., Zhou, M., Zeng, J., Dong, X., Zhang, R., et al. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.743 · Cited by 196

Transformer models used for text-based question answering systems

Nassiri, K., & Akhloufi, M. A. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04052-8 · Cited by 194

ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation

Malik, V., Sanjay, R., Nigam, S. K., Ghosh, K., Guha, S. K., Bhattacharya, A., & Modi, A. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.313 · Cited by 198

QuestEval: Summarization Asks for Fact-based Evaluation

Scialom, T., Dray, P.-A., Lamprier, S., Piwowarski, B., Staiano, J., Wang, A., & Gallinari, P. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.529 · Cited by 175

Visual Question Generation as Dual Task of Visual Question Answering

Li, Y., Duan, N., Zhou, B., Chu, X., Ouyang, W., Wang, X., & Zhou, M. (2018) · 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/cvpr.2018.00640 · Cited by 177

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

TER-Plus: paraphrase, semantic, and alignment enhancements to Translation Edit Rate

Snover, M., Madnani, N., Dorr, B. J., & Schwartz, R. M. (2009) · Machine Translation · doi:10.1007/s10590-009-9062-9 · Cited by 167

Machine translation evaluation versus quality estimation

Specia, L., Raj, D., & Turchi, M. (2010) · Machine Translation · doi:10.1007/s10590-010-9077-2 · Cited by 165

Assessing The Factual Accuracy of Generated Text

Goodrich, B., Rao, V., Liu, P. J., & Saleh, M. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330955 · Cited by 162

Ground Truth for Grammaticality Correction Metrics

Napoles, C., Sakaguchi, K., Post, M., & Tetreault, J. (2015) · doi:10.3115/v1/p15-2097 · Cited by 151

Active Learning and Crowd-Sourcing for Machine Translation

Ambati, V., Vogel, S., & Carbonell, J. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5dqoxv96xk95 · Cited by 150

Document Context Neural Machine Translation with Memory Networks

Maruf, S., & Haffari, G. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1118 · Cited by 156

A Survey on Document-level Neural Machine Translation

Maruf, S., Saleh, F., & Haffari, G. (2021) · ACM Computing Surveys · doi:10.1145/3441691 · Cited by 172

Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.291 · Cited by 149

Approaches to Human and Machine Translation Quality Assessment

Castilho, S., Doherty, S., Gaspari, F., & Moorkens, J. (2018) · Machine translation · doi:10.1007/978-3-319-91241-7_2 · Cited by 165

Text-to-Text Pre-Training for Data-to-Text Tasks

Kale, M., & Rastogi, A. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.14 · Cited by 145

CharacTer: Translation Edit Rate on Character Level

Wang, W., Peter, J.-T., Rosendahl, H., & Ney, H. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2342 · Cited by 134

Region-Object Relation-Aware Dense Captioning via Transformer

Shao, Z., Han, J., Marnerides, D., & Debattista, K. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3152990 · Cited by 134

Multimodal Video Description

Ramanishka, V., Das, A., Park, D. H., Venugopalan, S., Hendricks, L. A., Rohrbach, M., & Saenko, K. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2984066 · Cited by 132

Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs

Zhang, H., Liu, Z., Xiong, C., & Liu, Z. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.184 · Cited by 137

Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model

Sezgın, E., Sirrianni, J., & Linwood, S. L. (2022) · JMIR Medical Informatics · doi:10.2196/32875 · Cited by 129

A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

Loyola, P., Marrese-Taylor, E., & Matsuo, Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2045 · Cited by 128

A long video caption generation algorithm for big video data retrieval

Ding, S., Qu, S., Xi, Y., & Wan, S. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.054 · Cited by 128

Lattice Minimum Bayes-Risk decoding for statistical machine translation

Tromble, R. W., Kumar, S., Och, F. J., & Macherey, W. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613792 · Cited by 127

Discriminative corpus weight estimation for machine translation

Matsoukas, S., Rosti, A.-V. I., & Zhang, B. (2009) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1699571.1699605 · Cited by 123

Large Language Model Assisted Software Engineering: Prospects, Challenges, and a Case Study

Belzner, L., Gabor, T., & Wirsing, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46002-9_23 · Cited by 130

FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN

Ansari, E., Axelrod, A. E., Bách, N., Bojar, O., Cattoni, R., Dalvi, F., Durrani, N., Federico, M., Federmann, C., Gu, J., et al. (2020) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2020.iwslt-1.1 · Cited by 111

Changes to Captions: An Attentive Network for Remote Sensing Change Captioning

Chang, S., & Ghamisi, P. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3328224 · Cited by 127

High-Resolution Remote Sensing Image Captioning Based on Structured Attention

Zhao, R., Shi, Z., & Zou, Z. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3070383 · Cited by 117

METEOR, M-BLEU and M-TER

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

A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images

Messina, P., Pino, P., Parra, D., Soto, Á., Besa, C., Uribe, S. A., Andía, M. E., Cristián Tejos, N., Prieto, C., & Daniel Capurro, N. (2022) · ACM Computing Surveys · doi:10.1145/3522747 · Cited by 111

Automated audio captioning with recurrent neural networks

Drossos, K., Adavanne, S., & Virtanen, T. I. (2017) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2017.8170058 · Cited by 102

deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets

Galley, M., Brockett, C., Sordoni, A., Ji, Y., Auli, M., Quirk, C., Mitchell, M., Gao, J., & Dolan, B. (2015) · doi:10.3115/v1/p15-2073 · Cited by 101

Image Captioning: A Comprehensive Survey

Sharma, H., Agrahari, M., Singh, S. K., Firoj, M., & Mishra, R. K. (2020) · International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) · doi:10.1109/parc49193.2020.236619 · Cited by 102

Learning to rank with partially-labeled data

Duh, K., & Kirchhoff, K. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390379 · Cited by 101

Toward Subgraph-Guided Knowledge Graph Question Generation With Graph Neural Networks

Chen, Y., Wu, L., & Zaki, M. J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3264519 · Cited by 106

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

Qin, L., Galley, M., Brockett, C., Liu, X., Gao, X., Dolan, B., Choi, Y., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1539 · Cited by 100

Neural data-to-text generation: A comparison between pipeline and end-to-end architectures

Ferreira, T. C., van der Lee, C., van Miltenburg, E., & Krahmer, E. (2019) · Tilburg University Research Portal · Cited by 107

Automatic Source Code Summarization with Extended Tree-LSTM

Shido, Y., Kobayashi, Y., Yamamoto, A., Miyamoto, A., & Matsumura, T. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851751 · Cited by 102

Sampling alignment structure under a Bayesian translation model

DeNero, J., Bouchard‐Côté, A., & Klein, D. (2008) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1613715.1613758 · Cited by 97

TrustRank: Inducing Trust in Automatic Translations via Ranking

Soricut, R., & Echihabi, A. (2010) · Cited by 95

Text2Scene: Generating Compositional Scenes From Textual Descriptions

Tan, F., Feng, S., & Ordóñez, V. (2019) · 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/cvpr.2019.00687 · Cited by 95

High-Order Interaction Learning for Image Captioning

Wang, Y., Xu, N., Liu, A.-A., Li, W., & Zhang, Y. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3121062 · Cited by 95

POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training

Zhang, Y., Wang, G., Li, C., Gan, Z., Brockett, C., & Dolan, B. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.698 · Cited by 110

Large Language Models in Mental Health Care: A Scoping Review

Hua, Y., Liu, F., Yang, K., Li, Z., Na, H., Sheu, Y.-H., Zhou, P., Moran, L. V., Ananiadou, S., Clifton, D. A., et al. (2025) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-025-00363-y · Cited by 92

Global Visual Feature and Linguistic State Guided Attention for Remote Sensing Image Captioning

Zhang, Z., Zhang, W., Yan, M., Gao, X., Fu, K., & Sun, X. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3132095 · Cited by 96

Multimodal machine translation through visuals and speech

Sulubacak, U., Çağlayan, O., Grönroos, S.-A., Rouhe, A., Elliott, D., Specia, L., & Tiedemann, J. (2020) · Machine Translation · doi:10.1007/s10590-020-09250-0 · Cited by 104

A Decoupling Paradigm With Prompt Learning for Remote Sensing Image Change Captioning

Liu, C., Zhao, R., Chen, J., Qi, Z., Zou, Z., & Shi, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3321752 · Cited by 100

BEER: BEtter Evaluation as Ranking

Stanojević, M., & Sima’an, K. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3354 · Cited by 91

Inducing Sentence Structure from Parallel Corpora for Reordering

DeNero, J., & Uszkoreit, J. (2011) · Cited by 90

deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse TargetsPreprint

Galley, M., Brockett, C., Sordoni, A., Ji, Y., Auli, M., Quirk, C., Mitchell, M., Gao, J., & Dolan, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06863 · Cited by 90

Very Deep Transformers for Neural Machine TranslationPreprint

Liu, X., Duh, K., Liu, L., & Gao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07772 · Cited by 120

Semantic Noise Matters for Neural Natural Language Generation

Dušek, O., Howcroft, D. M., & Rieser, V. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8652 · Cited by 85

Semantic Graphs for Generating Deep Questions

Pan, L., Xie, Y., Feng, Y., Chua, T., & Kan, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.135 · Cited by 92

A survey of types of text noise and techniques to handle noisy text

Subramaniam, L. V., Roy, S., Faruquie, T. A., & Negi, S. (2009) · Analytics for Noisy Unstructured Text Data · doi:10.1145/1568296.1568315 · Cited by 85

Large-Scale Transfer Learning for Natural Language Generation

Golovanov, S., Kurbanov, R., Nikolenko, S., Truskovskyi, K., Tselousov, A., & Wolf, T. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1608 · Cited by 86

Ensemble-based deep reinforcement learning for chatbots

Cuayáhuitl, H., Lee, D., Ryu, S., Cho, Y. J., Choi, S., Indurthi, S., Yu, S., Choi, H., Hwang, I.-C., & Kim, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.007 · Cited by 87

Change-Agent: Toward Interactive Comprehensive Remote Sensing Change Interpretation and Analysis

Liu, C., Chen, K., Zhang, H., Qi, Z., Zou, Z., & Shi, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3425815 · Cited by 96

Automatic medical image interpretation: State of the art and future directions

Ayesha, H., Iqbal, S., Tariq, M., Abrar, M., Sanaullah, M., Abbas, I., Rehman, A. U., Niazi, M. F. K., & Hussain, S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107856 · Cited by 81

A Review on the Use of Large Language Models as Virtual Tutors

García-Méndez, S., de Arriba-Pérez, F., & Somoza-López, M. D. C. (2024) · Science & Education · doi:10.1007/s11191-024-00530-2 · Cited by 95

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