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

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

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

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

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 311

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

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 565

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 108

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 161

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 81

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

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 95

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 81

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 70

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 97

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 63

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 58

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 56

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 62

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 46

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 50

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

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 43

Topic Modeling with Contextualized Word Representation ClustersPreprint

Thompson, L., & Mimno, D. M. (2020) · arXiv.org · doi:10.48550/arxiv.2010.12626 · Cited by 111

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 71

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 37

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

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

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 51

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 42

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 34

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

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 28

Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis

He, J., Yanga, H., Zhang, C., Chen, H.-R., & Xua, Y.-S. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2105593 · Cited by 26

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

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 28

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

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

(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 29

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

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 28

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 43

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

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 22

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 24

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 24

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

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

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 22

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 21

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 18

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 19

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

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 25

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

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 16

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 16

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 18

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 18

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 27

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

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

An Interpretability Illusion for BERT

Bolukbasi, T., Pearce, A., Yuan, A., Coenen, A., Reif, E., Viégas, F., & Wattenberg, M. (2021) · arXiv · doi:10.48550/arxiv.2104.07143 · Cited by 113

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

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 15

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 15

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 19

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

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 13

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 24

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 11

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

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

Explainable Natural Language Processing

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

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

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

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 12

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

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 9

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

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

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

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

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

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

Phrase-level Active Learning for Neural Machine TranslationPreprint

Hu, J., & Neubig, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11375 · Cited by 10

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

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

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 7

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

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

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

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

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 8

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

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 8

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

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 9

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

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 7

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

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