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

Improve the Evaluation of Fluency Using Entropy for Machine Translation Evaluation MetricsPreprint

Yu, H., Wu, X., Jiang, W., Liu, Q., & Lin, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.02225 · Cited by 2

DiscoTK: Using Discourse Structure for Machine Translation EvaluationPreprint

Joty, S., Guzmán, F., Màrquez, L., & Nakov, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.12547 · Cited by 2

Question Generation for Supporting Informational Query Intents.Preprint

Yin, X., May, J., Li, Z., & Small, K. (2020) · arXiv (Cornell University) · Cited by 2

Beyond VQA: Generating Multi-word Answer and Rationale to Visual QuestionsPreprint

Dua, R., Kancheti, S. S., & Balasubramanian, V. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12852 · Cited by 2

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

Li, Y., Arnold, J., Yan, F., Shi, W., & Yu, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.01992 · Cited by 2

Guiding Topic Flows in the Generative Chatbot by Enhancing the ConceptNet with the Conversation CorporaPreprint

Si, P., Qiu, Y., Zhang, J., & Yang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05406 · Cited by 2

SoccerNet 2023 Challenges ResultsPreprint

Cioppa, A., Giancola, S., Somers, V., Magera, F., Zhou, X., Mkhallati, H., Deliège, A., Held, J., Hinojosa, C., Mansourian, A. M., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.06006 · Cited by 2

Natural Language Generation

Albert Gatt, A. (2025) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199384655.013.896 · Cited by 2

Amélioration des systèmes de traduction par analyse linguistique et thématique : application à la traduction depuis l'arabe

Gahbiche, S. (2013) · doi:10.70675/e8e9227dzeffez4b56za431zacbd1d2ccaea · Cited by 2

Extracting structured information from the textual description of geometry word problems

Boob, A., Bodakhe, P. D., Radke, M. A., & Deshpande, U. A. (2023) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3639233.3639255 · Cited by 1

Cross-Graph Knowledge Exchange for Personalized Response Generation in Dialogue Systems

Dong, Y., Qin, K., Ke, P., Liang, S., & Luo, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3541258 · Cited by 1

Non-Autoregressive Image Captioning with Multi-Label Classification and Self-Critical Sequence Training

Liu, Y., Yu, H. Z., Li, H., Zhang, X., Han, X., & Liu, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890521 · Cited by 1

A Review on Deep Learning Techniques for Image Caption Generation

Sai, C. V., Pramod, A., Shekar, A. R., & Jabbar, M. A. (2025) · International Conference on Computing Technologies & Data Communication (ICCTDC) · doi:10.1109/icctdc64446.2025.11158812 · Cited by 1

ChitraVivran: Real-Time Attention-Based Hindi Image Captioning with Boosted Contextual Descriptors

Sharma, H., Padha, D., & Singh, Y. P. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3779214 · Cited by 1

Analysis of Image Captioning Model Through Different Image Feature Extraction Networks

Raghavendra., M. J., Pasha, M. S., Lamani, A. D., Bushana Kumar, P., & Krupa, B. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280564 · Cited by 1

Multi-Perspective Inductive Answering of Subjective Questions on Products

Yu, J., Zhang, Y., Lai, H., Chen, W., Rao, Y., & Yin, J. P. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3608961 · Cited by 1

A Text Typological Assessment of Automatic Translation

Rasouli, F., Seyyedi, K., & Azaldin, A. H. (2026) · Cihan University-Erbil Journal of Humanities and Social Sciences · doi:10.24086/cuejhss.v10n1y2026.pp13-18 · Cited by 1

Evaluating the Impact of Input Format on LLM Performance: JSON vs. YAML vs. Text

Narvekar, A., Mukherjee, T., & Tanuwijaya, J. (2025) · IEEE International Conference on Future Machine Learning and Data Science (FMLDS) · doi:10.1109/fmlds67896.2025.00072 · Cited by 1

Enhancing Narrative Commonsense Reasoning With Multilevel Causal Knowledge

Mu, F., & Li, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3380851 · Cited by 2

Enhancing Emotional Support Capabilities of Large Language Models through Cascaded Neural Networks

Chen, K. Y., Kang, X., Lai, X., & Ni, Z. (2023) · International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI) · doi:10.1109/iccbd-ai62252.2023.00060 · Cited by 1

GKA-GPT: Graphical knowledge aggregation for multiturn dialog generation

Dong, Y., Qin, K., Liang, S., Raza, A., & Luo, G. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112763 · Cited by 1

Tab: template-aware bug report title generation via two-phase fine-tuned models

Liu, X., Xu, Y., Sun, W., Huang, N., Sun, S., Li, Q., Yang, D., & Yan, M. L. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00505-9 · Cited by 1

Attention-Driven Image Captioning for Mobile Accessibility of the Visually Impaired

Santi, D., Ilham, A. A., Syafaruddin, S., & Nurtanio, I. (2025) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v19i09.53441 · Cited by 1

Validity Checking and Repairing of Machine Generated Transfer-Type Word Problems

Kumar, S., & Kumar, P. S. (2024) · Applied Ontology · doi:10.1177/15705838241303829 · Cited by 1

Automated Code Comments Generation Using Large Language Models: Empirical Evaluation of T5 and BART

Ghale, D. P., & Dabbagh, M. (2025) · IEEE Access · doi:10.1109/access.2025.3597601 · Cited by 1

Vision to Specification: Automating the Transition from Conceptual Features to Functional Requirements

Lian, X., Wu, J., Gao, X., Wang, S., & Zhang, L. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3754450 · Cited by 1

Pharmaceutical chatbot assistant using generative AI and knowledge graph with specific pharmaceutical resources

Menaouer, B., Younes, C., Mokhtar, E. E., & Mohammed, S. (2026) · Iran Journal of Computer Science · doi:10.1007/s42044-026-00396-3 · Cited by 2

Valor32k-AVQA v2.0: Open-Ended Audio-Visual Question Answering Dataset and Benchmark

Riahi, I., Radman, A., Guo, Z., Hedjam, R., & Laaksonen, J. T. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758261 · Cited by 1

CyclicAligner: Knowledge-Enhanced Cyclical Alignment for Chest X-Ray Report Generation

Sun, J., Wang, P., Zhang, K., Tan, X., Fu, Z., & Yang, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356622 · Cited by 1

Small language models applied in text summarization task of health-related news to improve public health audit: an experimental case study

Guimarães, A., Júnior, M. C., de Almeida, S. S., de Araújo, G. G. F., Fontes, R. S., Santos, H. P., Alves, L. P. C. F., Matos, N., Valentim, R. A. D. M., & dos Santos, J. P. Q. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1708993 · Cited by 1

Spanish Triple-to-Text Benchmark on Low-Resource Large Language Models

Ramón-Ferrer, V., Badenes-Olmedo, C., & Corcho, Ó. (2026) · Semantic Web · doi:10.1177/22104968261440701 · Cited by 1

Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review

Rao, S., Chen, X. R., Deng, G., Xie, J., Jiang, T., Li, T., Zhang, Y., & Jiang, H. (2026) · JMIR Medical Informatics · doi:10.2196/91249 · Cited by 2

Rethinking Temporal Interaction with Selective State Space Modeling for Efficient Vision-Language Change Understanding in Remote Sensing

Tripathee, Y., Sultan, N., Iqbal, F., & Prom–on, S. (2026) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse68839.2026.11596626 · Cited by 1

Exploring the Use of Artificial Intelligence for Automatic Text Summarisation and Evaluation in eRulemaking

Promikyridis, R., Patsouras, C. F., Zeginis, D., Tambouris, E., & Tarabanis, K. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-24807-7_22 · Cited by 1

Do You See What I Say? Generalizable Deepfake Detection Based on Visual Speech Recognition

Reddy, S., Bora, M., Dhamija, T., Chopin, B., Balaji, P., Das, A., & Dantcheva, A. (2026) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2026.3719691 · Cited by 1

Evaluating Speech Synthesis Techniques for Medical Image Captions Using LLMs and GANs

Kapoor, L., & Kumari, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-26898-3_5 · Cited by 1

Automatic Machine Translation Evaluation with Part-of-Speech Information

Han, A. L.-F., Wong, D. F., Chao, L. S., & He, L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40585-3_16 · Cited by 1

ANN and case-based reasoning methods for English to Arabic machine translation

Akeel, M., & Mishra, R. B. (2014) · International Journal of Knowledge Engineering and Soft Data Paradigms · doi:10.1504/ijkesdp.2014.069294 · Cited by 1

A Diagnostic Evaluation Approach Targeting MT Systems for Indian Languages

Balyan, R., Naskar, S. K., Toral, A., & Chatterjee, N. (2012) · Cited by 1

RED: DCU-CASICT Participation in WMT2014 Metrics Task

Wu, X., Yu, H., & Liu, Q. (2014) · Cited by 1

Document-Level Machine Translation Evaluation Metrics Enhanced with Simplified Lexical Chain

Gong, Z., & Zhou, G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25207-0_35 · Cited by 1

Une étude en 3D de la paraphrase : types de corpus, langues et techniques

Bouamor, H., Max, A., & Vilnat, A. (2012) · Cited by 1

Speeding up the Implementation Process of a Shallow Transfer Machine Translation System.

Vičič, J., & Homola, P. (2010) · Cited by 1

ENTF: An Entropy-Based MT Evaluation Metric

Yu, H., Xu, W., Lin, S., & Liu, Q. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7134-8_7 · Cited by 1

Correlating decoding events with errors in Statistical Machine Translation

Avramidis, E., & Popović, M. (2014) · International conference natural language processing · Cited by 1

Forest to String Based Statistical Machine Translation with Hybrid Word Alignments

Pal, S., Naskar, S. K., & van Genabith, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75487-1_4 · Cited by 1

A Hierarchical Conditional Attention-Based Neural Networks for Paraphrase Generation

Nguyen-Ngoc, K., Le, A.-C., & Nguyen, V.-H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03014-8_14 · Cited by 1

Semantics and Relativity Expansion Based on Tag Recommendation with Time Degradation

Xiao, J., & He, L. (2011) · Energy Procedia · doi:10.1016/j.egypro.2011.11.244 · Cited by 1

Toward Collaborative Game Commentating Utilizing Pre-Trained Generative Language Models

Xu, J. H., Huang, H., Ling, X., & Paliyawan, P. (2022) · International Conference on Consumer Electronics · doi:10.1109/icce53296.2022.9730353 · Cited by 1

API + code = better code summary? insights from an exploratory study

Sarmah, P. P., & Chimalakonda, S. (2022) · doi:10.1145/3558489.3559075 · Cited by 1

Automated commentary generation based on FPS gameplay analysis

Mamoru, D. L. S., Panditha, A., Perera, W. A. S. S. J., & Ganegoda, G. U. (2022) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr57877.2022.9992987 · Cited by 1

Post-Attention Modulator for Dense Video Captioning

Guo, Z., Wang, T.-J. J., & Laaksonen, J. T. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956260 · Cited by 2

Semantic-Guided Selective Representation for Image Captioning

Li, Y., Ma, Y., Zhou, Y., & Yu, X. (2023) · IEEE Access · doi:10.1109/access.2023.3243952 · Cited by 1

Machine Learning for Natural Language Processing: Techniques and Evaluation

Raja, P., Kumar, S., Yadav, D. S., Kumar, A., & Kumar, R. K. (2023) · Journal of Artificial Intelligence Machine Learning and Neural Network · doi:10.55529/jaimlnn.33.1.9 · Cited by 1

Attending Local and Global Features for Image Caption Generation

Meghwal, V. K., Mittal, N., & Singh, G. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31407-0_47 · Cited by 1

Video Captioning using Spatio-temporal Graphs: An Encoder-Decoder Approach

Sharma, V., Singh, A. K., & Gaito, S. (2024) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets59351.2024.10427136 · Cited by 1

Visionary Narratives: Unveiling the Potential of an Automated Image Caption Generator through Deep Learning and Multidimensional Analysis

Keshamoni, K., & Priyanka, P. (2024) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi60426.2024.10503475 · Cited by 1

Image Caption Generator Using CNN and LSTM

Kapuriya, M., Lakkad, Z., & Shah, S. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24aug851 · Cited by 1

Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue Systems

Dong, Y., Qin, K., & Liang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9431-7_3 · Cited by 1

Memory-Tuning: A Unified Parameter-Efficient Tuning Method for Pre-Trained Language Models

Qi, W., Liu, R., Zuo, Y., Li, F., Chen, Y., & Wu, J. (2024) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3507558 · Cited by 1

SparrowVQE: Visual Question Explanation for Course Content Understanding

Li, J., Thota, M. K., Gokhman, R., Holik, R., & Zhang, Y. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825179 · Cited by 1

Attentional Seq2Seq Model for Arabic Opinion Question Generation

Ouahrani, L., & Bennouar, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-82112-7_9 · Cited by 1

Leveraging Synthetic Data for Improved Manipuri-English Code-Switched ASR

Singh, N. K., Madal, W., Devi, C. N., Pangsatabam, H., & Chanu, Y. J. (2025) · IEEE Access · doi:10.1109/access.2025.3538664 · Cited by 1

A Diagnostic Evaluation Approach for English to Hindi MT Using Linguistic Checkpoints and Error Rates

Balyan, R., Naskar, S. K., Toral, A., & Chatterjee, N. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37256-8_24 · Cited by 1

TrafficInternVL: Spatially-Guided Fine-Tuning with Caption Refinement for Fine-Grained Traffic Safety Captioning and Visual Question Answering

Phimsiri, S., Sunpawatr, S., Cherdchusakulchai, R., Kiawjak, P., Tosawadi, T., Tungjitnob, S., Trairattanapa, V., Vatathanavaro, S., Kudisthalert, W., Utintu, C., et al. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00559 · Cited by 1

SuMT: A Framework of Summarization and MT

Bouamor, H., Mohit, B., & Oflazer, K. (2013) · Cited by 1

A Case Study Towards Turkish Paraphrase Alignment

Demir, Ş., El-Kahlout, İ. D., & Ünal, E. (2013) · Cited by 1

Refinement by Filtering Translation Candidates and Similarity Based Approach to Expand Emotion Tagged Corpus

Matsumoto, K., Ren, F., Yoshida, M., & Kita, K. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-52758-1_15 · Cited by 1

Evaluation of machine translation output in context of inflectional languages

Munková, D., Munk, M., Kapusta, J., & Reichel, J. (2016) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/icaict.2016.7991658 · Cited by 1

Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment

Li, W., Langlais, P., & Jin, Y. (2017) · Workshop on Discourse in Machine Translation (DiscoMT) · doi:10.18653/v1/w17-4812 · Cited by 1

A Partially Rule-Based Approach to AMR Generation

Manning, E. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n19-3009 · Cited by 1

State-of-the-Art Overview

Sanchez, G., Agostini, L. V., & Marcon, C. A. M. (2019) · Springer eBooks · doi:10.1007/978-3-030-25927-3_3 · Cited by 1

An improved Image Description Method Using Recurrent Neural Network with Gated Recurrent Unit

Wang, H., & Li, K. (2019) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit48058.2019.8973133 · Cited by 1

Research on Machine Translation Automatic Evaluation Based on Extended Reference

Li, N., Su, W., Li, Y., Yu, H., Xu, W., & Gao, B. (2019) · IEEE International Conference on Computer and Communication Engineering Technology (CCET) · doi:10.1109/ccet48361.2019.8989400 · Cited by 1

Leveraging Neural Caption Translation with Visually Grounded Paraphrase Augmentation

Effendi, J., Sakti, S., Sudoh, K., & Nakamura, S. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edp7065 · Cited by 1

Automated Utterance Generation

Parikh, S., Vohra, Q., & Tiwari, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i08.7047 · Cited by 1

Extricating from GroundTruth: An Unpaired Learning Based Evaluation Metric for Image Captioning

Zhao, Z., Sun, Y.-L., Wang, N.-X., & Tian, W.-D. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206632 · Cited by 1

Learning to Describe Editing Activities in Collaborative Environments: A Case Study on GitHub and Wikipedia.

Marrese-Taylor, E., Loyola, P., Balazs, J., & Matsuo, Y. (2020) · Pacific Asia Conference on Language, Information, and Computation · Cited by 1

Do Encoder Representations of Generative Dialogue Models have sufficient summary of the Information about the task ?

Parthasarathi, P., Pineau, J., & Chandar, S. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.50 · Cited by 1

Attribute Centered Multimodal Response Generation in a Dialogue System

Firdaus, M., Shandilya, A. P., Pujari, S. P., & Ekbal, A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533312 · Cited by 1

A Multi-layer Feature Parallel Processing Method for Image Captioning

He, C., Tong, Q., Yang, X., Wang, J., & Zhu, T. (2021) · doi:10.1109/icnlp52887.2021.00049 · Cited by 1

Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streaming

Huang, H., Xu, J. H., Ling, X., & Paliyawan, P. (2022) · International Conference on Consumer Electronics · doi:10.1109/icce53296.2022.9730453 · Cited by 1

Evaluating Semantic Autocompletion of Business Processes with Domain Experts

Goldstein, M., & González-Álvarez, C. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678821 · Cited by 1

Machine Translation Performance Prediction System: Optimal Prediction for Optimal Translation

Biçici, E. (2022) · SN Computer Science · doi:10.1007/s42979-022-01183-0 · Cited by 1

Conceptual Representation and Evaluation of an FPS Game Commentary Generator

Mamoru, D. L. S., Panditha, A., Perera, W. A. S. S. J., & Ganegoda, G. U. (2022) · International Conference on Image Processing and Robotics (ICIPRoB) · doi:10.1109/iciprob54042.2022.9798735 · Cited by 1

Noise-Robust Semi-supervised Multi-modal Machine Translation

Li, L., Hu, K., Tayir, T., Liu, J., & Lee, K. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20865-2_12 · Cited by 1

Prior tissue knowledge-driven contrastive learning for brain CT report generation

Shi, Y., Ji, J., Zhang, X., Liu, Y., Wang, Z., & Xu, H. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01289-w · Cited by 2

A Comprehensive Guide to Deep Neural Network-Based Image Captions

Pandey, P., & Brahmbhatt, K. N. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24mar1602 · Cited by 1

Assessing Fine-Tuning Efficacy in LLMs: A Case Study with Learning Guidance Chatbots

Bayraktar, R., Sarıtürk, B., & Erdem, M. E. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24may1600 · Cited by 2

Question Answering Against Very-Large Text CollectionsPreprint

Derczynski, L., Shaw, R. A., Solway, B., & Wang, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.7157 · Cited by 1

Phrase Alignment Models for Statistical Machine Translation

DeNero, J. (2010) · eScholarship (California Digital Library) · Cited by 1

Evaluation of automatically reformulated questions in question series

Shaw, R., Solway, B., Gaizauskas, R., & Greenwood, M. (2008) · doi:10.3115/1641451.1641459 · Cited by 1

Capturing Lexical Variation in MT Evaluation Using Automatically Built Sense-Cluster Inventories

Apidianaki, M., He, Y., & Way, A. (2009) · Institutional Repositories DataBase (IRDB) · Cited by 1

Mixed up with machine Translation: Multi-word Units Disambiguation Challenge.

Barreiro, A. M., Monti, J., Elia, A., & Monteleone, M. (2010) · HAL · Cited by 1

Incorporating visual information into neural machine translation

Calixto, I. (2017) · Arrow@dit (Dublin Institute of Technology) · Cited by 1

The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQAPreprint

Jolly, S., Pezzelle, S., Klein, T., Dengel, A. R., & Nabi, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04344 · Cited by 1

ReDecode Framework for Iterative Improvement in Paraphrase GenerationPreprint

Aggarwal, M., Kumari, N., Bansal, A., & Krishnamurthy, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.04454 · Cited by 1

Towards Human-Friendly Referring Expression GenerationPreprint

Tanaka, M. N., Itamochi, T., Narioka, K., Sato, I., Ushiku, Y., & Harada, T. (2018) · arXiv (Cornell University) · Cited by 1

Coling 2008: Proceedings of the 2nd workshop on Information Retrieval for Question Answering

Greenwood, M. (2008) · Cited by 1

Better Understanding Hierarchical Visual Relationship for Image CaptionPreprint

Fei, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.01881 · Cited by 1

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

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