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The Significance of Recall in Automatic Metrics for MT Evaluation

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End-to-end Adversarial Learning for Generative Conversational AgentsPreprint

Ludwig, O. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10122 · Cited by 10

Multimodal Machine Translation through Visuals and Speech

Sulubacak, U., Çağlayan, O., Grönroos, S.-A., Rouhe, A., Elliott, D., Specia, L., & Tiedemann, J. (2019) · Spiral (Imperial College London) · doi:10.5281/zenodo.3690790 · Cited by 10

Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian ResponsePreprint

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Temporal Sub-sampling of Audio Feature Sequences for Automated Audio CaptioningPreprint

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Joint Retrieval and Generation Training for Grounded Text Generation.Preprint

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Sometimes We Want Ungrammatical Translations

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Cross-region feature fusion with geometrical relationship for OCR-based image captioning

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Referential Translation Machines for Predicting Translation Quality and Related Statistics

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Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputs.

Stasimioti, M., Sosoni, V., Kermanidis, K. L., & Mouratidis, D. (2020) · Cited by 9

MEE : An Automatic Metric for Evaluation Using Embeddings for Machine Translation

Mukherjee, A., Ala, H., Shrivastava, M., & Sharma, D. M. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00042 · Cited by 9

Generalizing Sub-sentential Paraphrase Acquisition across Original Signal Type of Text Pairs

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Learning distribution of video captions using conditional GAN

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5G INSTRUCT Forge: An Advanced Data Engineering Pipeline for Making LLMs Learn 5G

Said, A. I. A., Mekrache, A., Boutiba, K., Ramantas, K., Ksentini, A., & Rahmani, M. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3516055 · Cited by 10

Further Steps Towards A Standard Testbed For Optical Music Recognition.

Hajič, J., Novotný, J., Pecina, P., & Pokorný, J. (2016) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1418160 · Cited by 9

Machine Translation Evaluation Metric Based on Dependency Parsing Model

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CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

Maksoud, S., Wiliem, A., Zhao, K., Zhang, T., Wu, L. Y., & Lovell, B. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_48 · Cited by 9

Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations

Cabezudo, M. A. S., Mille, S., & Pardo, T. A. S. (2019) · Workshop on Multilingual Surface Realisation (MSR) · doi:10.18653/v1/d19-6313 · Cited by 9

CBAG: Conditional biomedical abstract generation

Sybrandt, J., & Safro, I. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253905 · Cited by 9

Agnostic learning with multiple objectives

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Knowledge-Grounded Dialogue Generation with Term-level De-noising

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SummerTime: Text Summarization Toolkit for Non-experts

Ni, A., Azerbayev, Z., Mutuma, M., Feng, T., Zhang, Y., Tao, Y., Awadallah, A. H., & Radev, D. (2021) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2021.emnlp-demo.37 · Cited by 9

Fine-grained Factual Consistency Assessment for Abstractive Summarization Models

Zhang, S., Niu, J., & Wei, C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.9 · Cited by 9

UAT: Universal Attention Transformer for Video Captioning

Im, H., & Choi, Y. S. (2022) · Sensors · doi:10.3390/s22134817 · Cited by 9

Choosing the Best MT Programs for CLIR Purposes – Can MT Metrics Be Helpful?

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Neural Caption Generation for News Images

Batra, V., He, Y., & Vogiatzis, G. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/38koo4iw6neu · Cited by 9

Improving the Translation Environment for Professional Translators

Vandeghinste, V., Vanallemeersch, T., Augustinus, L., Bulté, B., Eynde, F. V., Pelemans, J., Verwimp, L., Wambacq, P., Heyman, G., Moens, M., et al. (2019) · Informatics · doi:10.3390/informatics6020024 · Cited by 13

Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics

Boyanov, M., Koychev, I. K., Nakov, P., Moschitti, A., & Martino, G. D. S. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_018 · Cited by 9

Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets

Mao, H.-R., & Lee, H.-Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1611 · Cited by 9

Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)

Dušek, O., Sevegnani, K., Konstas, I., & Rieser, V. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8644 · Cited by 9

Conversational Intelligence Challenge: Accelerating Research with Crowd Science and Open Source

Burtsev, M., & Logacheva, V. (2020) · AI Magazine · doi:10.1609/aimag.v41i3.5324 · Cited by 9

A Novel Actor Dual-Critic Model for Remote Sensing Image Captioning

Chavhan, R., Banerjee, B., Zhu, X. X., & Chaudhuri, S. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412486 · Cited by 9

Compound Processing for Phrase-Based Statistical Machine Translation

Stymne, S. (2009) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 9

Genre Oriented Summarization

Goldstein-Stewart, J. (2009) · Cited by 9

WaveTransformer: A Novel Architecture for Audio Captioning Based on Learning Temporal and Time-Frequency InformationPreprint

Tran, A., Drossos, K., & Virtanen, T. I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11098 · Cited by 9

From Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text GenerationPreprint

Zhou, J., Gong, H., Nanniyur, S., & Bhat, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06541 · Cited by 9

Interdisciplinary Development and Fine-Tuning of CARDIO, a Large Language Model for Cardiovascular Health Education in HIV Care: Tutorial

Rullo, R., Maatouk, A., Huang, T., Chen, J., Qiu, W., O’Connor, G., Womack, J. A., Sadak, T. I., Rodriguez, C., Carneiro, P. B., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/77053 · Cited by 8

DFKI's system for WMT16 IT-domain task, including analysis of systematic errors

Avramidis, E., Burchardt, A., Macketanz, V., & Srivastava, A. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2329 · Cited by 8

A Smart System for Text-Lifelog Generation from Wearable Cameras in Smart Environment Using Concept-Augmented Image Captioning with Modified Beam Search Strategy

Vo-Ho, V.-K., Luong, Q.-A., Nguyen, D.-T., Tran, M.-K., & Tran, M. (2019) · Applied Sciences · doi:10.3390/app9091886 · Cited by 8

Spatial-aware topic-driven-based image Chinese caption for disaster news

Zhou, J., Zhu, Y., Zhang, Y., Yang, C., & Pan, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-08072-w · Cited by 8

Pre-trained Language Models

Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_2 · Cited by 12

Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding

Xu, M., Islam, M., Glocker, B., & Ren, H. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3276361 · Cited by 8

Vision-Text Cross-Modal Fusion for Accurate Video Captioning

Ouenniche, K., Țapu, R., & Zaharia, T. B. (2023) · IEEE Access · doi:10.1109/access.2023.3324052 · Cited by 8

Simple Token-Level Confidence Improves Caption Correctness

Petryk, S., Whitehead, S., Gonzalez, J. E., Darrell, T., Rohrbach, A., & Rohrbach, M. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00564 · Cited by 8

Comparative Analysis of News Articles Summarization using LLMs

Archanaa., N., Shivanesh, B., T, S. K. J. D., Bharathi Mohan, G., & Doss, S. (2024) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit62007.2024.10673458 · Cited by 8

Enhanced Dense Image Captioning Based On Transformers

Goswami, T., Potu, S., Reddy, K. P., Prashamsa, V., Rao, K. R. M., & Tripathi, M. K. (2024) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca63461.2024.10800880 · Cited by 8

Pixels to prose: A comprehensive survey of image captioning techniques with deep learning and generative artificial intelligence

Sharma, A., Singh, H., & Pant, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132385 · Cited by 11

Generating targeted paraphrases for improved translation

Madnani, N., & Dorr, B. J. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2483669.2483673 · Cited by 8

Translation of Medical Texts using Neural Networks

Wołk, K., & Marasek, K. P. (2016) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.2016100104 · Cited by 8

ConvAI2 Dataset of Non-goal-Oriented Human-to-Bot Dialogues

Logacheva, V., Malykh, V., Litinsky, A., & Burtsev, M. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8_11 · Cited by 8

Aspect-Level Sentiment-Controllable Review Generation with Mutual Learning Framework

Chen, H., Lin, Y., Qi, F., Hu, J., Li, P., Zhou, J., & Sun, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17497 · Cited by 8

Evaluating the Efficacy of Summarization Evaluation across Languages

Koto, F., Lau, J. H., & Baldwin, T. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.71 · Cited by 11

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

Paul, M. D., Finch, A., & Sumita, E. (2010) · Cited by 8

An overview of the phrase-based statistical machine translation techniques

Costa‐jussà, M. R. (2012) · The Knowledge Engineering Review · doi:10.1017/s026988891200029x · Cited by 8

Sentence Similarity-Based Source Context Modelling in PBSMT

Haque, R., Naskar, S. K., Way, A., Costa‐jussà, M. R., & Banchs, R. E. (2010) · International Conference on Asian Language Processing · doi:10.1109/ialp.2010.45 · Cited by 8

Statistical transfer systems for French--English and German--English machine translation

Hanneman, G., Huber, E., Agarwal, A., Ambati, V., Parlikar, A., Peterson, E. J., & Lavie, A. (2008) · Conference on Machine Translation (WMT) · doi:10.3115/1626394.1626418 · Cited by 8

Machine translation and audiovisual products: a case study

Martín-Mor, A., & Sánchez-Gijón, P. (2016) · The Journal of Specialised Translation · doi:10.26034/cm.jostrans.2016.283 · Cited by 8

Copy Mechanism and Tailored Training for Character-Based Data-to-Text Generation

Roberti, M., Bonetta, G., Cancelliere, R., & Gallinari, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46147-8_39 · Cited by 8

Look, Read and Ask: Learning to Ask Questions by Reading Text in Images

Jahagirdar, S., Gangisetty, S., & Mishra, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86549-8_22 · Cited by 8

Bilingual Random Walk Models for Automated Grammar Correction of ESL Author-Produced Text

West, R. M., Park, Y., & Lévy, R. (2011) · Workshop on Innovative Use of NLP for Building Educational Applications · Cited by 8

Point at the Triple: Generation of Text Summaries from Knowledge Base Triples

Vougiouklis, P., Maddalena, E., Hare, J. S., & Simperl, E. P. B. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11694 · Cited by 8

New Statistical And Syntactic Models For Machine Translation

Khalilov, M. (2010) · Cited by 8

Large Language Models in Randomized Controlled Trials Design: Observational Study

Jin, L., Ong, J. C. L., Elangovan, K., Ke, Y., Pyle, A., Ting, D. S. W., & Liu, N. (2025) · Journal of Medical Internet Research · doi:10.2196/67469 · Cited by 7

POS-Tagging based Neural Machine Translation System for European Languages using Transformers

Ganesh, P., Rawal, B. S., Peter, A., & Giri, A. (2021) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2021.18.5 · Cited by 7

Improving Many-to-Many Neural Machine Translation via Selective and Aligned Online Data Augmentation

Zhang, W., Dai, L.-R., Liu, J., & Wang, S. (2023) · Applied Sciences · doi:10.3390/app13063946 · Cited by 7

End-to-End Non-Autoregressive Image Captioning

Yu, H., Liu, Y., Qi, B., Hu, Z.-L., & Liu, H. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095338 · Cited by 7

NLP-Based Fusion Approach to Robust Image Captioning

Ricci, R., Melgani, F., Marcato, J., & Gonçalves, W. N. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3413323 · Cited by 8

Research on Tibetan Tourism Viewpoints Information Generation System Based on LLM

Qi, J., Yan, S., Zhang, W., Zhang, Y., Liu, Z., & Wang, K. (2024) · International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC) · doi:10.1109/icwoc62055.2024.10684948 · Cited by 8

Investigating Large Language Models for Prompt-Based Open-Ended Question Generation in the Technical Domain

Maity, S., Deroy, A., & Sarkar, S. (2024) · SN Computer Science · doi:10.1007/s42979-024-03464-2 · Cited by 7

VideoA11y: Method and Dataset for Accessible Video Description

Li, C., Padmanabhuni, S., Cheema, M. S., Seifi, H., & Fazli, P. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714096 · Cited by 7

DCU Participation in WMT2013 Metrics Task

Wu, X., Yu, H., & Liu, Q. (2013) · Cited by 7

Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language

Sekizawa, Y., Kajiwara, T., & Komachi, M. (2017) · Cited by 7

NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager

Yusupov, I., & Kuratov, Y. (2018) · International Conference on Computational Linguistics · Cited by 7

A Survey of Dialogue System Evaluation

Fan, Y., & Luo, X. (2020) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai50040.2020.00182 · Cited by 7

Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Models

Xu, J. H., Shinden, K., & Kato, M. P. (2021) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce53005.2021.9621890 · Cited by 7

Generating Answer Candidates for Quizzes and Answer-Aware Question Generators

Vachev, K., Hardalov, M., Karadzhov, G., Georgiev, G., Releva AI, S., Koychev, I. K., & Nakov, P. (2021) · Student Research Workshop .../Proceedings of the Student Research Workshop ... · doi:10.26615/issn.2603-2821.2021_028 · Cited by 7

Document-Level Neural Machine Translation with Hierarchical Modeling of Global Context

Tan, X., Zhang, L.-Y., & Zhou, G. (2022) · Journal of Computer Science and Technology · doi:10.1007/s11390-021-0286-3 · Cited by 7

Diversity-Controllable and Accurate Audio Captioning Based on Neural Condition

Xu, X., Wu, M., & Yu, K. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746834 · Cited by 7

Fully Attentional Network for Low-Resource Academic Machine Translation and Post Editing

İlhami, S. E. L., & Hanbay, D. (2022) · Applied Sciences · doi:10.3390/app122211456 · Cited by 7

Subjective and objective evaluation of English to Urdu Machine translation

Gupta, V., Joshi, N., & Mathur, I. (2013) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2013.6637405 · Cited by 7

Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention

Tong, B., Klinkigt, M., Iwayama, M., Yanase, T., Kobayashi, Y., Sahu, A., & Vennelakanti, R. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098132 · Cited by 7

End-to-end trainable system for enhancing diversity in natural language generation

Deriu, J., & Cieliebak, M. (2017) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-4889 · Cited by 7

Evaluating Text Generation from Discourse Representation Structures

Wang, C., van Noord, R., Bisazza, A., & Bos, J. (2021) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2021.gem-1.8 · Cited by 8

Agent metaphor for machine translation mediated communication

Shi, C., Lin, D., & Ishida, T. (2013) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2449396.2449407 · Cited by 7

DTED: Evaluation of Machine Translation Structure Using Dependency Parsing and Tree Edit Distance

McCaffery, M., & Nederhof, M.-J. (2016) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w16-2340 · Cited by 7

Multiple Videos Captioning Model for Video Storytelling

Han, S. H., Go, B.-W., & Choi, H. (2019) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2019.8679213 · Cited by 7

Automatically generated parallel treebanks and their exploitability in machine translation

Tinsley, J. F., & Way, A. (2009) · Machine Translation · doi:10.1007/s10590-009-9068-3 · Cited by 7

Resourcing machine translation with parallel treebanks

Tinsley, J. F. (2009) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 7

Listen carefully and tell: an audio captioning system based on residual learning and gammatone audio representationPreprint

Perez-Castanos, S., Naranjo-Alcazar, J., Zuccarello, P., & Cobos, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15406 · Cited by 7

Towards Machine Translation for the Kurdish LanguagePreprint

Ahmadi, S., & Masoud, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06041 · Cited by 8

Recycling texts: human evaluation of example-based machine translation subtitles for DVD

Flanagan, M. (2009) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 7

Enhancing AI-based Generation of Software Exploits with Contextual Information

Liguori, P., Improta, C., Natella, R., Čukić, B., & Cotroneo, D. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00027 · Cited by 6

Menstrual Health Education Using a Specialized Large Language Model in India: Development and Evaluation Study of MenstLLaMA

Adhikary, P. K., Motiyani, I., Öke, G., Joshi, M., Pathak, K., Singh, S. M., & Chakraborty, T. (2025) · Journal of Medical Internet Research · doi:10.2196/71977 · Cited by 6

Multimodal LLM Question Generation for Children's Art Engagement via Museum Social Robots

Ferrato, A., Cristina Gena, A., Limongelli, C., & Sansonetti, G. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3708319.3733663 · Cited by 6

Fine-Grained Image Captioning by Ranking Diffusion Transformer

Wan, J., Gan, M., Zhang, L., Zhou, J., Liu, J., Du, B., & Chen, C. L. P. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3641303 · Cited by 6

Towards Personalized AI Education: Context‐Aware Retrieval‐Augmented Generation With Grade‐Level LLM Adaptation

Nguyen, V. Q., Tran, N. H., Dao, K. A., & Nguyen, P. V. (2026) · Computer Applications in Engineering Education · doi:10.1002/cae.70153 · Cited by 7

VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding

Kim, Y., Abdelrahman, A. S., & Abdel-Aty, M. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00085 · Cited by 6

Keyword-driven image captioning via Context-dependent Bilateral LSTM

Zhang, X., He, S., Song, X., Wei, P., Jiang, S., Ye, Q., Jiao, J., & Lau, R. W. H. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019525 · Cited by 6

Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training

Chen, L., Li, J., Gong, Z., Chen, B., Luo, W., Zhang, M., & Zhou, G. (2021) · doi:10.18653/v1/2021.acl-long.222 · Cited by 6

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

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