Unsupervised Domain Clusters in Pretrained Language Models

Attention Is All You Need

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Adam: A Method for Stochastic Optimization

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

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Latent dirichlet allocation

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Distributed Representations of Words and Phrases and their Compositionality

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RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Introduction to information retrieval

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Deep Contextualized Word Representations

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

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XLNet: Generalized Autoregressive Pretraining for Language Understanding

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Unsupervised Cross-lingual Representation Learning at Scale

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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

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fairseq: A Fast, Extensible Toolkit for Sequence Modeling

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A Call for Clarity in Reporting BLEU Scores

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Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks

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What Does BERT Look at? An Analysis of BERT’s Attention

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Parallel Data, Tools and Interfaces in OPUS

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BERT Rediscovers the Classical NLP Pipeline

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KenLM: Faster and Smaller Language Model Queries

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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

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Transfer Learning in Natural Language Processing

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Using the Output Embedding to Improve Language Models

Press, O., & Wolf, L. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2025 · Cited by 642 · see citing works

Intelligent Selection of Language Model Training Data

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Domain Adaptation via Pseudo In-Domain Data Selection

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Findings of the 2019 Conference on Machine Translation (WMT19)

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Assessing BERT's Syntactic Abilities

Goldberg, Y. (2019) · arXiv · doi:10.48550/arxiv.1901.05287 · Cited by 385 · see citing works

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual GeneralizationPreprint

Hu, J., Ruder, S., Siddhant, A., Neubig, G., Fırat, O., & Johnson, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11080 · Cited by 335 · see citing works

Facebook FAIR’s WMT19 News Translation Task Submission

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A Survey of Domain Adaptation for Neural Machine TranslationPreprint

Chu, C.-H., & Wang, R. (2018) · International Conference on Computational Linguistics · doi:10.48550/arxiv.1806.00258 · Cited by 301 · see citing works

The Frequency and Use of Lexical Bundles in Conversation and Academic Prose

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Multi-Domain Neural Machine Translation through Unsupervised Adaptation

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Curriculum Learning for Domain Adaptation in Neural Machine Translation

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

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Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora

Junczys-Dowmunt, M. (2018) · Proceedings of the Third Conference on Machine Translation: Shared Task Papers · doi:10.18653/v1/w18-6478 · Cited by 106 · see citing works

Active learning for statistical phrase-based machine translation

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Sentence Embedding for Neural Machine Translation Domain Adaptation

Wang, R., Finch, A., Utiyama, M., & Sumita, E. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2089 · Cited by 90 · see citing works

Domain Adaptation with BERT-based Domain Classification and Data Selection

Ma, X., Xu, P., Wang, Z., Nallapati, R. M., & Xiang, B. (2019) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/d19-6109 · Cited by 89 · see citing works

Non-Parametric Adaptation for Neural Machine Translation

Bapna, A., & Firat, O. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-1191 · Cited by 87 · see citing works

Domain Robustness in Neural Machine Translation

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Domain Adaptation of Neural Machine Translation by Lexicon Induction

Hu, J., Xia, M., Neubig, G., & Carbonell, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1286 · Cited by 63 · see citing works

Dynamic Topic Adaptation for Phrase-based MT

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A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output

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Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation

Wang, W., Caswell, I. R., & Chelba, C. I. (2019) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p19-1123 · Cited by 49 · see citing works

Survey of data-selection methods in statistical machine translation

Eetemadi, S., Lewis, W. D., Toutanova, K., & Radha, H. (2015) · Machine Translation · doi:10.1007/s10590-015-9176-1 · Cited by 47 · see citing works

Does more data always yield better translations

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Neural Machine Translation Training in a Multi-Domain ScenarioPreprint

Sajjad, H., Durrani, N., Dalvi, F., Belinkov, Y., & Vogel, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08712 · Cited by 43 · see citing works

Split and Rephrase: Better Evaluation and Stronger Baselines

Aharoni, R., & Goldberg, Y. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2114 · Cited by 40 · see citing works

When Does Unsupervised Machine Translation Work?

Marchisio, K., Duh, K., & Koehn, P. (2020) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2020.wmt-1.68 · Cited by 37 · see citing works

Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings

Dou, Z.-Y., Hu, J.-J., Anastasopoulos, A., & Neubig, G. (2019) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d19-1147 · Cited by 34 · see citing works

Learning to select data for transfer learning with Bayesian Optimization

Ruder, S., & Plank, B. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1038 · Cited by 31 · see citing works

What’s in a Domain? Analyzing Genre and Topic Differences in Statistical Machine Translation

van der Wees, M., Bisazza, A., Weerkamp, W., & Monz, C. (2015) · doi:10.3115/v1/p15-2092 · Cited by 22 · see citing works

Extracting In-domain Training Corpora for Neural Machine Translation Using Data Selection Methods

Silva, C. C., Liu, C.-H., Poncelas, A., & Way, A. (2018) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w18-6323 · Cited by 22 · see citing works

Emerging Cross-lingual Structure in Pretrained Language ModelsPreprint

Wu, S., Conneau, A., Li, H., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.01464 · Cited by 21 · see citing works

Data Selection with Feature Decay Algorithms Using an Approximated Target SidePreprint

Poncelas, A., Wenniger, G. M. D. B., & Way, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03039 · Cited by 12 · see citing works

The OPUS Resource Repository: An Open Package for Creating Parallel Corpora and Machine Translation Services.

Aulamo, M., & Tiedemann, J. (2019) · DSpace repository (University of Tartu) · Cited by 12 · see citing works

Adapting to All Domains at Once: Rewarding Domain Invariance in SMT

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Data selection for NMT using Infrequent n-gram Recovery

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Domain Differential Adaptation for Neural Machine Translation

Dou, Z.-Y., Wang, X., Hu, J., & Neubig, G. (2019) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/d19-5606 · Cited by 11 · see citing works

Cynical Selection of Language Model Training DataPreprint

Axelrod, A. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02279 · Cited by 11 · see citing works

When and Why is Unsupervised Neural Machine Translation Useless?Preprint

Kim, Y., Graça, M., & Ney, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10581 · Cited by 10 · see citing works

Neural Networks Classifier for Data Selection in Statistical Machine Translation

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Data Selection for IT Texts using Paragraph Vector

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Data Selection with Cluster-Based Language Difference Models and Cynical SelectionPreprint

Santamaría, L., & Axelrod, A. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04900 · Cited by 8 · see citing works

Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning

Jacovi, A., Niu, G., Goldberg, Y., & Sugiyama, M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.47 · Cited by 4 · see citing works

What’s in a domain?: Towards fine-grained adaptation for machine translation

van der Wees, M. (2017) · UvA-DARE (University of Amsterdam) · Cited by 4 · see citing works

Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks

Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.740 · Cited by 2,103 · see citing works

MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

Hazarika, D., Zimmermann, R., & Poria, S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413678 · Cited by 1,073 · see citing works

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

Hedderich, M. A., Lange, L., Adel, H., Strötgen, J., & Klakow, D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.201 · Cited by 310 · see citing works

Neural Unsupervised Domain Adaptation in NLP—A Survey

Ramponi, A., & Plank, B. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.603 · Cited by 253 · see citing works

MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment AnalysisPreprint

Hazarika, D., Zimmermann, R., & Poria, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03545 · Cited by 154 · see citing works

Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Xu, L., Xie, H., Qin, S. J., Tao, X., & Wang, F. L. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3657354 · Cited by 318 · see citing works

Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey

Saunders, D. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13566 · Cited by 96 · see citing works

In-context Examples Selection for Machine Translation

Agrawal, S., Zhou, C., Lewis, M., Zettlemoyer, L., & Ghazvininejad, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.564 · Cited by 106 · see citing works

T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

Ushio, A., & Camacho-Collados, J. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-demos.7 · Cited by 71 · see citing works

X-Class: Text Classification with Extremely Weak Supervision

Wang, Z., Mekala, D., & Shang, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.242 · Cited by 70 · see citing works

Neural Machine Translation with Monolingual Translation Memory

Cai, D., Wang, Y., Li, H., Lam, W. P., & Liu, L. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.567 · Cited by 81 · see citing works

DEMix Layers: Disentangling Domains for Modular Language Modeling

Gururangan, S., Lewis, M., Holtzman, A., Smith, N. A., & Zettlemoyer, L. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.407 · Cited by 67 · see citing works

Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics

Zhang, Z., Fang, M., Chen, L., & Rad, M. R. N. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.285 · Cited by 64 · see citing works

Efficient Nearest Neighbor Language Models

He, J., Neubig, G., & Berg-Kirkpatrick, T. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.461 · Cited by 59 · see citing works

Explainable Misinformation Detection Across Multiple Social Media Platforms

Joshi, G., Srivastava, A., Yagnik, B., Hasan, M. M., Saiyed, Z., Gabralla, L. A., Abraham, A., Walambe, R. A., & Kotecha, K. V. (2023) · IEEE Access · doi:10.1109/access.2023.3251892 · Cited by 60 · see citing works

Train No Evil: Selective Masking for Task-Guided Pre-Training

Gu, Y., Zhang, Z., Wang, X., Liu, Z., & Sun, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.566 · Cited by 55 · see citing works

PharmBERT: a domain-specific BERT model for drug labels

ValizadehAslani, T., Shi, Y., Ren, P., Wang, J., Zhang, Y., Hu, M., Zhao, L., & Liang, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad226 · Cited by 50 · see citing works

First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT

Müller, B., Elazar, Y., Sagot, B., & Seddah, D. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.189 · Cited by 55 · see citing works

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

Bu, X., Peng, J., Yan, J., Tan, T., & Zhang, Z. (2021) · 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/cvpr46437.2021.00034 · Cited by 45 · see citing works

AdaPrompt: Adaptive Model Training for Prompt-based NLP

Chen, Y., Liu, Y., Li, D., Wang, S., Zhu, C., Zeng, M., & Zhang, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.448 · Cited by 45 · see citing works

DECN: Dialogical emotion correction network for conversational emotion recognition

Lian, Z., Liu, B., & Tao, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.017 · Cited by 42 · see citing works

Computational Language Modeling and the Promise of In Silico Experimentation

Jain, S., Vo, V. A., Wehbe, L., & Huth, A. G. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00101 · Cited by 37 · see citing works

Topic Modeling with Contextualized Word Representation ClustersPreprint

Thompson, L., & Mimno, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12626 · Cited by 63 · see citing works

Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models

Pang, J., Ye, F., Wong, D. F., Yu, D., Shi, S., Tu, Z., & Wang, L. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00730 · Cited by 49 · see citing works

Fast Nearest Neighbor Machine Translation

Meng, Y., Li, X., Zheng, X., Wu, F. Q., Sun, X., Zhang, T., & Li, J. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.47 · Cited by 33 · see citing works

AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models

Chronopoulou, A., Peters, M., Fraser, A., & Dodge, J. (2023) · Findings of the Association for Computational Linguistics: EACL 2023 · doi:10.18653/v1/2023.findings-eacl.153 · Cited by 30 · see citing works

Domain Divergences: A Survey and Empirical Analysis

Kashyap, A. R., Hazarika, D., Kan, M., & Zimmermann, R. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.147 · Cited by 33 · see citing works

OntoChat: A Framework for Conversational Ontology Engineering Using Language Models

Zhang, B., Carriero, V. A., Schreiberhuber, K., Tsaneva, S., González, L. S., Kim, J., & de Berardinis, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78952-6_10 · Cited by 28 · see citing works

Efficient Hierarchical Domain Adaptation for Pretrained Language Models

Chronopoulou, A., Peters, M. E., & Dodge, J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.96 · Cited by 34 · see citing works

Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection

Zhang, J., Hashimoto, K., Wan, Y., Liu, Z., Liu, Y., Xiong, C., & Yu, P. (2022) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2022.nlp4convai-1.2 · Cited by 28 · see citing works

Multimodal Sentiment Analysis Based on Cross-Modal Attention and Gated Cyclic Hierarchical Fusion Networks

Quan, Z., Sun, T., Su, M., & Wei, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4767437 · Cited by 25 · see citing works

FEDS-ICL: Enhancing translation ability and efficiency of large language model by optimizing demonstration selection

Zhu, S., Pan, L., & Xiong, D. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103825 · Cited by 25 · see citing works

Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis

He, J., Yanga, H., Zhang, C., Chen, H., & Xua, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2105593 · Cited by 25 · see citing works

Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment

Bodrunova, S. S., Orekhov, A. V., Blekanov, I. S., Сергеевич, Л. Н., & Tarasov, N. A. (2020) · Future Internet · doi:10.3390/fi12090144 · Cited by 24 · see citing works

CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding

Wright, D., & Augenstein, I. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.157 · Cited by 24 · see citing works

Goal-Driven Explainable Clustering via Language Descriptions

Wang, Z., Shang, J., & Zhong, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.657 · Cited by 31 · see citing works

Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

Tang, Y., Dehaghani, M. R., Sajadi, P., & Wang, G. G. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02402-6 · Cited by 24 · see citing works

(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance

Goldman, O., Guriel, D., & Tsarfaty, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-short.96 · Cited by 23 · see citing works

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

Zheng, X. W., Zhang, Z., Huang, S., Chen, B., Xie, J., Luo, W., & Chen, J. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.358 · Cited by 23 · see citing works

Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation

Chronopoulou, A., Stojanovski, D., & Fraser, A. (2023) · doi:10.18653/v1/2023.loresmt-1.5 · Cited by 24 · see citing works

Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning

Alves, D., Guerreiro, N., Alves, J., Pombal, J., Rei, R., de Souza, J. G. C., Colombo, P., & Martins, A. F. T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.744 · Cited by 26 · see citing works

Continual Learning for Neural Machine Translation

Cao, Y., Wei, H., Chen, B., & Wan, X. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.310 · Cited by 21 · see citing works

Learning from Unlabelled Data for Clinical Semantic Textual Similarity

Wang, Y., Verspoor, K. M., & Baldwin, T. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.25 · Cited by 20 · see citing works

Prompting Neural Machine Translation with Translation Memories

Reheman, A., Zhou, T., Luo, Y., Yang, D., Xiao, T., & Zhu, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26585 · Cited by 22 · see citing works

DirectProbe: Studying Representations without Classifiers

Zhou, Y., & Srikumar, V. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.401 · Cited by 21 · see citing works

Efficient Machine Translation Domain Adaptation

Martins, P., Marinho, Z., & Martins, A. F. T. (2022) · doi:10.18653/v1/2022.spanlp-1.3 · Cited by 19 · see citing works

DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference

Murty, S., Hashimoto, T., & Manning, C. D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.88 · Cited by 19 · see citing works

Learning Kernel-Smoothed Machine Translation with Retrieved Examples

Jiang, Q., Wang, M., Cao, J., Cheng, S., Huang, S., & Li, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.579 · Cited by 18 · see citing works

Chunk-based Nearest Neighbor Machine Translation

Martins, P. H., Marinho, Z., & Martins, A. F. T. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.284 · Cited by 17 · see citing works

Unsupervised multi-sense language models for natural language processing tasks

Roh, J., Park, S., Kim, B.-K., Oh, S.-H., & Lee, S.-Y. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.05.023 · Cited by 17 · see citing works

The Trade-offs of Domain Adaptation for Neural Language Models

Grangier, D., & Iter, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.264 · Cited by 16 · see citing works

GAIA-Universe: Everything is Super-Netify

Peng, J., Chang, Q., Yin, H., Bu, X., Sun, J., Xie, L., Zhang, X., Tian, Q. C., & Zhang, Z. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3276392 · Cited by 16 · see citing works

Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations

Fei, Y., Zhao, M., Nie, P., Wattenhofer, R. P., & Sachan, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.587 · Cited by 20 · see citing works

Neural Machine Translation with Monolingual Translation MemoryPreprint

Cai, D., Yan, W., Li, H., Lam, W. P., & Liu, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11269 · Cited by 16 · see citing works

DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog

Hung, C.-C., Lauscher, A., Ponzetto, S. P., & Glavaš, G. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.72 · Cited by 15 · see citing works

The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention

Ophir, Y., Tikochinski, R., Klomek, A. B., & Reichart, R. (2021) · Clinical Psychological Science · doi:10.1177/21677026211022013 · Cited by 14 · see citing works

Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with AdaptersPreprint

Stickland, A. C., Bérard, A., & Nikoulina, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.09574 · Cited by 16 · see citing works

Nearest Neighbor Knowledge Distillation for Neural Machine Translation

Yang, Z., Sun, R., & Wan, X. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.406 · Cited by 14 · see citing works

Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection

Uppaal, R., Hu, J., & Li, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.717 · Cited by 18 · see citing works

Domain Transfer based Data Augmentation for Neural Query Translation

Yao, L., Yang, B., Zhang, H., Chen, B., & Luo, W. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.399 · Cited by 12 · see citing works

Domain Confused Contrastive Learning for Unsupervised Domain Adaptation

Long, Q., Luo, T., Wang, W., & Pan, S. J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.217 · Cited by 15 · see citing works

An Interpretability Illusion for BERTPreprint

Bolukbasi, T., Pearce, A., Yuan, A., Coenen, A., Reif, E., Viégas, F. B., & WATTENBERG, M. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07143 · Cited by 32 · see citing works

Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications

Lucy, L., Dodge, J., Bamman, D., & Keith, K. A. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.433 · Cited by 14 · see citing works

A Conceptual Framework for Subdomain Specific Pre-Training of Large Language Models for Green Claim Detection

Moodaley, W., & Telukdarie, A. (2023) · European Journal of Sustainable Development · doi:10.14207/ejsd.2023.v12n4p319 · Cited by 12 · see citing works

Genre as Weak Supervision for Cross-lingual Dependency Parsing

Müller-Eberstein, M., van der Goot, R., & Plank, B. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.393 · Cited by 12 · see citing works

Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits

Kreutzer, J., Vilar, D., & Sokolov, A. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.274 · Cited by 11 · see citing works

Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions

Gu, S., Hu, B., & Feng, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.111 · Cited by 11 · see citing works

Speech Separation With Pretrained Frontend to Minimize Domain Mismatch

Wang, W., Pan, Z., Li, X., Wang, S., & Li, H. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3446242 · Cited by 12 · see citing works

Augmenting Large Language Model Translators via Translation Memories

Mu, Y., Reheman, A., Cao, Z., Fan, Y., Li, B., Li, Y., Xiao, T., Zhang, C., & Zhu, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.653 · Cited by 13 · see citing works

An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training

Arumae, K., Sun, Q., & Bhatia, P. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.394 · Cited by 10 · see citing works

cpgQA: A Benchmark Dataset for Machine Reading Comprehension Tasks on Clinical Practice Guidelines and a Case Study Using Transfer Learning

Mahbub, M., Begoli, E., Martins, S. B., Peluso, A., Tamang, S. R., & Peterson, G. D. (2023) · IEEE Access · doi:10.1109/access.2023.3235265 · Cited by 9 · see citing works

Dual Knowledge Distillation for neural machine translation

Wan, Y., Zhang, W., Li, Z., Zhang, H., & Li, Y. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101583 · Cited by 11 · see citing works

Explainable Natural Language Processing

Søgaard, A. (2021) · Synthesis lectures on human language technologies · doi:10.2200/s01118ed1v01y202107hlt051 · Cited by 32 · see citing works

Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters

Ngo, N. T., Phung, D., & Nguyen, T. H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.351 · Cited by 10 · see citing works

Multimodal Sentiment Analysis Based on Nonverbal Representation Optimization Network and Contrastive Interaction Learning

Quan, Z., Sun, T., Su, M., Wei, J., Zhang, X., & Zhong, S. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945514 · Cited by 10 · see citing works

m4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter

Lai, W., Chronopoulou, A., & Fraser, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.315 · Cited by 8 · see citing works

Comparing Span Extraction Methods for Semantic Role Labeling

Zhang, Z., Strubell, E., & Hovy, E. H. (2021) · Workshop on Structured Prediction for NLP (SPNLP) · doi:10.18653/v1/2021.spnlp-1.8 · Cited by 8 · see citing works

Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts

Drinkall, F., Zohren, S., & Pierrehumbert, J. B. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.105 · Cited by 8 · see citing works

Subset Retrieval Nearest Neighbor Machine Translation

Deguchi, H., Watanabe, T., Matsui, Y., Utiyama, M., Tanaka, H., & Sumita, E. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.10 · Cited by 8 · see citing works

Robust fine-tuning for low-resource NLP: Combining adversarial and metric-based learning to mitigate overfitting

Choi, K., & Ko, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127737 · Cited by 8 · see citing works

WDSRL: Multi-Domain Neural Machine Translation With Word-Level Domain-Sensitive Representation Learning

Man, Z., Huang, Z., Zhang, Y., Yu, L., Chen, Y., Chen, Y., & Xu, J. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3336515 · Cited by 7 · see citing works

Rethinking Translation Memory Augmented Neural Machine Translation

Hao, H., Huang, G., Liu, L., Zhang, Z., Shi, S., & Wang, R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.162 · Cited by 7 · see citing works

Entity Extraction in Low Resource Domains with Selective Pre-training of Large Language Models

Mahapatra, A., Nangi, S. R., Garimella, A., & Natarajan, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.61 · Cited by 7 · see citing works

MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities

Kargupta, P., Komarlu, T., Yoon, S., Wang, X., & Han, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.708 · Cited by 8 · see citing works

Phrase-level Active Learning for Neural Machine TranslationPreprint

Hu, J., & Neubig, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11375 · Cited by 7 · see citing works

Subset Retrieval Nearest Neighbor Machine Translation

Deguchi, H., Watanabe, T., Matsui, Y., Utiyama, M., Tanaka, H., & Sumita, E. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.374 · Cited by 6 · see citing works

EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering

Duan, Z., Zhang, H., Wang, C., Wang, Z., Chen, B., & Zhou, M. (2021) · doi:10.18653/v1/2021.acl-long.230 · Cited by 7 · see citing works

Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training

Zhu, P., & Hauff, C. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.183 · Cited by 6 · see citing works

Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions

Hoang, C., Sachan, D. S., Mathur, P., Thompson, B. J., & Federico, M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.22 · Cited by 6 · see citing works

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 · see citing works

On the Complementarity of Data Selection and Fine Tuning for Domain AdaptationPreprint

Iter, D., & Grangier, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.07591 · Cited by 8 · see citing works

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 · see citing works

Multi-Label Text Classification Based on Label-Sentence Bi-Attention Fusion Network with Multi-Level Feature Extraction

Li, A., & Zhang, L. (2025) · Electronics · doi:10.3390/electronics14010185 · Cited by 5 · see citing works

Exploring a method for extracting concerns of multiple breast cancer patients in the domain of patient narratives using BERT and its optimization by domain adaptation using masked language modeling

Watabe, S., Watanabe, T., Yada, S., Aramaki, E., Yajima, H., Kizaki, H., & Hori, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305496 · Cited by 5 · see citing works

What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation

Zhu, W., Huang, S., Lv, Y., Zheng, X., & Chen, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.177 · Cited by 5 · see citing works

MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents

Razdaibiedina, A., & Brechalov, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.46 · Cited by 5 · see citing works

Residual Learning of Neural Text Generation with n-gram Language Model

Li, H., Cai, D., Xu, J., & Watanabe, T. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.109 · Cited by 6 · see citing works

BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition

Pavlova, V., & Makhlouf, M. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.31 · Cited by 5 · see citing works

A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches

Wang, Z., Wang, T., Mekala, D., & Shang, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.244 · Cited by 5 · see citing works

Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers

Hung, C.-C., Lauscher, A., Hovy, D., Ponzetto, S. P., & Glavaš, G. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.116 · Cited by 4 · see citing works

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 · see citing works

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 · see citing works

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

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

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