Gardner, Matt

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

Universal Adversarial Triggers for Attacking and Analyzing NLP

Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (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)

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 642

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (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)

Topic ModelingNatural Language Processing Techniques

Cited by 393

Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 306

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 238

Do NLP Models Know Numbers? Probing Numeracy in Embeddings

Wallace, E., Wang, Y., Li, S., Singh, S., & Gardner, M. (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 227

Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases

Gardner, M., Talukdar, P., Krishnamurthy, J., & Mitchell, T. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingAdvanced Graph Neural Networks

Cited by 189

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

Compositional Questions Do Not Necessitate Multi-hop Reasoning

Min, S., Wallace, E., Singh, S., Gardner, M., Hajishirzi, H., & Zettlemoyer, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingInformation Retrieval and Search Behavior

Cited by 138

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingInformation Retrieval and Search Behavior

Cited by 138

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Wallace, E., Tuyls, J., Wang, J., Subramanian, S., Gardner, M., & Singh, S. (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): System Demonstrations

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 116

Evaluating Question Answering Evaluation

Chen, A., Stanovsky, G., Singh, S., & Gardner, M. (2019) · Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Topic ModelingNatural Language Processing Techniques

Cited by 105

Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning

Razeghi, Y., Logan IV, R. L., Gardner, M., & Singh, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Topic ModelingNatural Language Processing Techniques

Cited by 89

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

Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Razeghi, Y., Logan, R. L., Gardner, M., & Singh, S. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 53

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (2021) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0