Peters, Matthew

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)

Topic ModelingNatural Language Processing Techniques

Cited by 11,983

Longformer: The Long-Document Transformer

Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2,281

Knowledge Enhanced Contextual Word Representations

Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 661

Semi-supervised sequence tagging with bidirectional language models

Peters, M., Ammar, W., Bhagavatula, C., & Power, R. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 655

Dissecting Contextual Word Embeddings: Architecture and Representation

Peters, M., Neumann, M., Zettlemoyer, L., & Yih, W.-T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 434

To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks

Peters, M. E., Ruder, S., & Smith, N. A. (2019) · Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 432

Construction of the Literature Graph in Semantic Scholar

Ammar, W., Groeneveld, D., Bhagavatula, C., Beltagy, I., Crawford, M., Downey, D., Dunkelberger, J., Elgohary, A., Feldman, S., Ha, V., et al. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

scientometrics and bibliometrics researchTopic Modeling

Cited by 358

Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling

Logan, R., Liu, N. F., Peters, M. E., Gardner, M., & Singh, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 168

Competency Problems: On Finding and Removing Artifacts in Language Data

Gardner, M., Merrill, W., Dodge, J., Peters, M., Ross, A., Singh, S., & Smith, N. A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 81

Dolma: an open corpus of three trillion tokens for language model pretraining research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 56

Extracting Latent Steering Vectors from Pretrained Language Models

Subramani, N., Suresh, N., & Peters, M. E. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 31

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

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 30

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Ivison, H., Wang, Y., Pyatkin, V., Lambert, N., Peters, M., Dasigi, P., Jang, J., Wadden, D., Smith, N. A., Beltagy, I., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 8