Chen, Danqi

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

Digital Humanities and ScholarshipComputational and Text Analysis Methods

Cited by 16,515

Dense Passage Retrieval for Open-Domain Question Answering

Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 5,311

Dense passage retrieval for open-domain question answering

Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 3,575

SimCSE: Simple Contrastive Learning of Sentence Embeddings

Gao, T., Yao, X., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 2,851

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,827

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,827

Reading Wikipedia to answer open-domain questions

Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 1,546

Making Pre-trained Language Models Better Few-shot Learners

Gao, T., Fisch, A., & Chen, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 1,380

Observed versus latent features for knowledge base and text inference

Toutanova, K., & Chen, D. (2015) · Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality

Advanced Graph Neural NetworksTopic Modeling

Cited by 1,070

CoQA: A Conversational Question Answering Challenge

Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics

Topic ModelingSpeech and dialogue systems

Cited by 975

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

A Frustratingly Easy Approach for Entity and Relation Extraction

Zhong, Z., & Chen, D. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 531

Factual Probing Is [MASK]: Learning vs. Learning to Recall

Zhong, Z., Friedman, D., & Chen, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 274

Enabling Large Language Models to Generate Text with Citations

Gao, T., Yen, H., Yu, J., & Chen, D. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 223

Enabling Large Language Models to Generate Text with Citations

Gao, T., Yen, H., Yu, J., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 216

Structured Pruning Learns Compact and Accurate Models

Xia, M., Zhong, Z., & Chen, D. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Advanced Neural Network ApplicationsTopic Modeling

Cited by 136

Making Pre-trained Language Models Better Few-shot Learners

Gao, T., Fisch, A., & Chen, D. (2020) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 124

Simple Entity-Centric Questions Challenge Dense Retrievers

Sciavolino, C., Zhong, Z., Lee, J., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 98

Retrieval-based Language Models and Applications

Asai, A., Min, S., Zhong, Z., & Chen, D. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 81

Training language models with memory augmentation

Zhong, Z., Lei, T., & Chen, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 60

Training Language Models with Memory Augmentation

Zhong, Z., Lei, T., & Chen, D. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 60

Adapting Language Models to Compress Contexts

Chevalier, A., Wettig, A., Ajith, A., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 59

Adapting Language Models to Compress Contexts

Chevalier, A., Wettig, A., Ajith, A., & Chen, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 59

MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

Zhong, Z., Wu, Z., Manning, C. D., Potts, C., & Chen, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 55