Unsupervised Domain Clusters in Pretrained Language Models

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Latent dirichlet allocation

(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515

Introduction to information retrieval

(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687

Towards Unified Multi-Domain Machine Translation With Mixture of Domain Experts

Lu, J., & Zhang, J. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3316451 · Cited by 5

“Average” Approximates “First Principal Component”? An Empirical Analysis on Representations from Neural Language Models

Wang, Z., Dong, C., & Shang, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.453 · Cited by 4

Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining

Chen, M., Jayakumar, B., Johnston, M. O., Mahmoodi, S. E., & Pressel, D. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-industry.23 · Cited by 4

OSP-Class: Open Set Pseudo-labeling with Noise Robust Training for Text Classification

Kim, D., Koo, J., & Kim, U. M. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020273 · Cited by 5

Addressing Domain Mismatch in Unsupervised Neural Machine Translation

Lin, Y., Wang, R., & Chu, C. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3527896 · Cited by 4

WOT-Class: Weakly Supervised Open-world Text Classification

Wang, T., Wang, Z., Liu, W., & Shang, J. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615109 · Cited by 5

Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge

(2022) · doi:10.18653/v1/2022.spanlp-1 · Cited by 4

CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models

Ormazabal, A., Artetxe, M., & Agirre, E. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.180 · Cited by 12

N-Gram Nearest Neighbor Machine Translation

Lv, R., Guo, J., Wang, R., Tan, X., Qin, T., & Liu, Q. (2024) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3407537 · Cited by 4

TeC: A Novel Method for Text Clustering with Large Language Models Guidance and Weakly-Supervised Contrastive Learning

Yang, C., Cao, B., & Fan, J. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31419 · Cited by 5

Predicting stress–strain relationships of additively manufactured materials under compression and tension using transfer learning and Wasserstein distance-based dataset pruning

Duan, C., & Wu, D. (2025) · Materials & Design · doi:10.1016/j.matdes.2025.114209 · Cited by 3

Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction

Lee, G., Yang, S., & Choi, E. (2021) · doi:10.18653/v1/2021.acl-short.94 · Cited by 3

Local Byte Fusion for Neural Machine Translation

Sreedhar, M. N., Wan, X., Cheng, Y., & Hu, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.397 · Cited by 7

Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting

Wang, K., Xie, J., Zhang, Y., & Zhao, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.333 · Cited by 3

A Comparison of Topic Modeling Approaches Using Networked Discussion Forum Posts From the City-data.com Corpus

Omizo, R. M. (2024) · Journal of Open Humanities Data · doi:10.5334/johd.182 · Cited by 3

Specializing Multi-domain NMT via Penalizing Low Mutual Information

Lee, J., Kim, H., Cho, H., Choi, E., & Park, C. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.680 · Cited by 3

How people talk about each other: Modeling Generalized Intergroup Bias and Emotion

Govindarajan, V. S., Atwell, K., Sinno, B., Alikhani, M., Beaver, D. I., & Li, J. J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.183 · Cited by 4

Sequential Reptile: Inter-Task Gradient Alignment for Multilingual LearningPreprint

Lee, S., Lee, H., Lee, J., & Hwang, S. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02600 · Cited by 4

Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

(2023) · doi:10.18653/v1/2023.loresmt-1 · Cited by 3

Redacción Académica Asistida por IA: Límites, potencial y responsabilidad

Guillén, S. R. G. R., Alcívar-Chávez, A. C., Sánchez, E. J. S., & Jácome-Gómez, J. R. (2026) · doi:10.70171/bjt3g968 · Cited by 2

On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling

Zhang, Z., Strubell, E., & Hovy, E. H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.503 · Cited by 2

Real: A Representative Error-Driven Approach for Active Learning

Chen, C., Wang, Y., Liao, L., Chen, Y., & Du, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43412-9_2 · Cited by 2

There’s No Data like Better Data: Using QE Metrics for MT Data Filtering

Peter, J.-T., Vilar, D., Deutsch, D., Finkelstein, M., Juraska, J., & Freitag, M. (2023) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2023.wmt-1.50 · Cited by 3

An Ensemble Strategy with Gradient Conflict for Multi-Domain Neural Machine Translation

Man, Z., Zhang, Y., Yu, L., Chen, Y., Chen, Y., & Xu, J. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3638248 · Cited by 2

A multi-agent system based on HNC for domain-specific machine translation

Li, M., & Zhang, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-03414-9 · Cited by 2

Multi-domain adaptation for named entity recognition with multi-aspect relevance learning

Li, J., Liu, J., Chen, Y., & Xu, J. (2022) · Language Resources and Evaluation · doi:10.1007/s10579-022-09590-8 · Cited by 2

Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer

Kashyap, A. R., Mehnaz, L., Malik, B., Waheed, A., Hazarika, D., Kan, M., & Shah, R. R. (2021) · National University of Singapore · Cited by 2

On the Universality of Deep Contextual Language ModelsPreprint

Bhatt, S., Goyal, P., Dandapat, S., Choudhury, M., & Sitaram, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.07140 · Cited by 4

UD-MULTIGENRE – a UD-Based Dataset Enriched with Instance-Level Genre Annotations

Danilova, V. V., & Stymne, S. (2023) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2023.mrl-1.19 · Cited by 1

Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison

Bouthors, M., Crego, J., & Yvon, F. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.190 · Cited by 8

Constructing ensembles for hate speech detection

Kucukkaya, I. E., & Toraman, Ç. (2024) · Natural language processing. · doi:10.1017/nlp.2024.44 · Cited by 3

Robot Control via Natural Instructions Empowered by Large Language Model

Wu, Z., Shu, P., Li, Y., Li, Q., Liu, T., & Li, X. R. (2024) · Springer eBooks · doi:10.1007/978-3-031-66656-8_19 · Cited by 1

Progressive and Consistent Subword Regularization for Neural Machine Translation

Gao, Y., Luo, Y., Zhang, Q., Shao, H., Xiao, T., & Zhu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9437-9_25 · Cited by 1

Mapping symbolic ties in U.S. sociology: Evidence from doctoral dissertation vocabularies

Yan, A. X., Bao, H., Leppard, T. R., & Davis, A. P. (2026) · Poetics · doi:10.1016/j.poetic.2026.102077 · Cited by 1

UDON: Unsupervised Data SelectiON for Biomedical Entity Recognition

Akdemir, A., & Shibuya, T. (2021) · International Conference on Computing and Big Data (ICCBD) · doi:10.1145/3507524.3507525 · Cited by 2

Dynamic Fusion Nearest Neighbor Machine Translation via Dempster-Shafer Theory

Yang, Z., Hou, H., Sun, S., Wu, N., Wang, Y., Jian, W., & Wang, P. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-7960-6_9 · Cited by 1

An Effective Ensemble Model Related to Incremental Learning in Neural Machine Translation

Shi, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30105-6_10 · Cited by 1

CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels

Cho, H., Kim, Y., & Lee, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.239 · Cited by 3

Continual Domain Adaption for Neural Machine Translation

Yang, M., Zhang, H., Yu, C., & Geng, G. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8145-8_33 · Cited by 1

Knowledge Graph Guided Neural Machine Translation with Dynamic Reinforce-selected Triples

Zhao, Y., Kang, X., Zhang, Y., Zhang, J., Zhou, Y., & Zong, C. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3696664 · Cited by 1

Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources

Xu, Y., Lu, D., Liang, J., Zhao, J. Z., Wang, X., Wu, H., Chen, K., Liu, L., Xin, Y., Liu, X., et al. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679835 · Cited by 2

Unsupervised Domain Adaptation for Entity Blocking Leveraging Large Language Models

Wang, Y., & Yan, M. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825234 · Cited by 1

DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation

Park, C., Kim, H., Calapodescu, I., Cho, H. C., & Nikoulina, V. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.141 · Cited by 1

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation-Nearest Neighbor Neural Machine Translation

Cao, Z., Yang, B., Lin, H., Wu, S., Wei, X., Liu, D., Xie, J., Zhang, M., & Su, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.321 · Cited by 3

Cultural Ties in American SociologyPreprint

Yan, X., Bao, H., Leppard, T. R., & Davis, A. P. (2024) · doi:10.31235/osf.io/qvyj8 · Cited by 1

SIFT-HF Scale-Invariant Feature-based similarity Threshold from Human FeedbackPreprint

Cyrusian, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5241660 · Cited by 1

Multi-domain Neural Machine Translation

Pham, M.-Q. (2021) · theses.fr (ABES) · doi:10.70675/17e2ca68z8662z4bb9zb875zac511563f5c6 · Cited by 23

Foundations of GenIR

Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv · doi:10.48550/arxiv.2501.02842

Robust Document Representations using Latent Topics and MetadataPreprint

Raman, N., Nourbakhsh, A., Shah, S., & Veloso, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12681 · Cited by 1

Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encodersPreprint

Brendan, C., & Evans, J. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.05928

GAIA: A Transfer Learning System of Object Detection that Fits Your NeedsPreprint

Bu, X., Peng, J., Yan, J., Tan, T., & Zhang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11346

Genre as Weak Supervision for Cross-lingual Dependency ParsingPreprint

Müller-Eberstein, M., van der Goot, R., & Plank, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04733

Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)

(2021) · doi:10.18653/v1/2021.spnlp-1

When Polysemy Matters: Modeling Semantic Categorization with Word Embeddings

Soper, E., & Koenig, J.-P. (2022) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2022.starsem-1.10 · Cited by 1

A Novel Distilled Generative Essay Polish System via Hierarchical Pre-Training

Yang, Q., Zhang, L., Zhang, Y., Gao, J., & Wang, S. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892086

Modeling the Role of Polysemy in Verb Categorization

Soper, E., & Koenig, J.-P. (2023) · Experiments in Linguistic Meaning · doi:10.3765/elm.2.5379

Mixture of Domain Experts for Language Understanding: an Analysis of Modularity, Task Performance, and Memory Tradeoffs

Kleiner, B., FitzGerald, J., Khan, H., & Tur, G. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022866

Iterative Nearest Neighbour Machine Translation for Unsupervised Domain Adaptation

Huang, H., Wu, S., Liang, X., Zhou, Z., Yang, M., & Zhao, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.840

Unsupervised Domain Adaptation using Lexical Transformations and Label Injection for Twitter Data

Gupta, A., Liu, X., & Shah, S. (2023) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2023.wassa-1.17

A Data Selection Method for Domain-specific Machine Translation

Ye, N., Jiang, L., & Cai, D. (2023) · International Conference on Frontiers of Electronics, Information and Computation Technologies · doi:10.1109/icfeict59519.2023.00069

Probabilistic Robustness for Data Filtering

Yu, Y., Khan, A. R., Khadivi, S., & Xu, J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.214

METAPROBE: A Representation- and Task-Agnostic Probe

Zhou, Y., & Srikumar, V. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.18

Revisiting Source Context in Nearest Neighbor Machine Translation

Li, X., Li, P., & Hu, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.503

Towards Concept-Aware Large Language Models

Shani, C., Vreeken, J., & Shahaf, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.877 · Cited by 2

Semi-supervised method for improving general-purpose and domain-specific textual corpora labels

Babikov, I., Kovalchuk, S. V., & Soldatov, I. K. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.12.018

A Novel Method of Translation Memory to Improve Machine Translation

Yue, P., Niu, Y., Tao, M., Zheng, W., Li, Y., & Shi, X. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-9243-0_15 · Cited by 1

Towards Faster k-Nearest-Neighbor Machine Translation

Shi, X., Liang, Y., Xu, J., & Chen, Y. (2024) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2024.41111 · Cited by 1

Tibetan Web Text Clustering Based on Contrast Learning

Zhang, R., & Sun, Y. (2023) · International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI) · doi:10.1109/iccbd-ai62252.2023.00127

Continual Learning with Confidence-based Multi-teacher Knowledge Distillation for Neural Machine Translation

Guo, J., Liang, Y., & Xu, J. (2024) · doi:10.1109/icnlp60986.2024.10692378

Beyond Copy–Pasting—Contextualizing LLMs for Secure Code Generation

Arjun, S., Majumdar, D., Nabin, A. Z., Sharma, A. K., & Raheman, S. M. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-5791-6_15

Foundations of Generative Information Retrieval

Ai, Q., Zhan, J., & Liu, Y. (2024) · The information retrieval series · doi:10.1007/978-3-031-73147-1_2 · Cited by 1

Data Selection for Close-Domain Data in Medical Continual Pretraining: A Case Study on Data Selection via Importance Resampling (DSIR)

Ardchon, C., Udomcharoenchaikit, C., Chaiwong, N., & Khanarsa, P. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6400-9_6

CCKA: Continual Cross-Domain Knowledge Adaptation for Multi-Domain Machine Translation

Man, Z., Zhang, Y., Chen, Y., Chen, Y., & Xu, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3594292

Classification of Language Technology Resources Using Multi-Kernel Fuzzy Support Vector Machine and the Access Control for Data Security

Zhang, Y., Ran, L., Li, Z., & He, Y. (2025) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd67701.2025.11198001

Domain Adaptive Machine Translation with Synthetic Feedback for Large Language Models

Yang, X., Zhan, R., Wu, J., Zhang, Y., Liu, X., Chao, L. S., Huo, Y., & Wong, D. F. (2026) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3787498

Neural Transfer Learning for Domain Adaptation in Natural Language Processing

Meftah, S. (2021) · theses.fr (ABES) · doi:10.70675/9111562bzfa37z4fd8zae6bz2e8ae8d5d403

How Can We Make Language Models Better at Handling the Diversity and Variability of Natural Languages ?

Müller, B. (2022) · theses.fr (ABES) · doi:10.70675/6984dc63z4731z4eabzb3fazaf5d012fe143

Automated Reasoning and Topic Detection in Text Clustering

Sharma, D. R., & Vanitha, K. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898815318126040007

Back-Translation and Unsupervised Domain Adaptation for Machine Translation of Arabic Dialects

Salim, A., & Brink, W. (2026) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3820768

LLM-Assisted Multi-domain Translation with Composed In-Domain Experts

Shi, X., Zhou, A., Chen, Y., Huang, K., Han, W., & Xu, J. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-0199-0_5

Bag-of-words or embedding-based topic models? Comparing LDA, BERTopic, and six other methods

Lamirel, J.-C., Lareau, F., Prouteau, T., & Malaterre, C. (2026) · Scientometrics · doi:10.1007/s11192-026-05745-4 · Cited by 2

Nearest Neighbor Machine Translation

Khandelwal, U., Fan, A., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2020) · arXiv · doi:10.48550/arxiv.2010.00710 · Cited by 165

Translation Transformers Rediscover Inherent Data DomainsPreprint

Del, M., Korotkova, E., & Fishel, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.07864 · Cited by 6

Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection

Vu, T.-T., He, X., Phung, D., & Haffari, G. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.268 · Cited by 6

Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation

Duh, K., Neubig, G., Sudoh, K., & Tsukada, H. (2013) · Cited by 107

H.: Adaptation data selection using neural language models: Experiments in machine translation

Duh, K., Neubig, G., Sudoh, K., & Tsukada, H. (2013)

Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation

Ji, S., Sinulingga, H. R., & Kwon, D. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3cvwjib8w9d5