Radev, Dragomir

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 1,034

How to analyze political attention with minimal assumptions and costs

Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010) · American Journal of Political Science

Computational and Text Analysis MethodsTopic Modeling

Cited by 671

SummEval: Re-evaluating Summarization Evaluation

Fabbri, A. R., Kryściński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingAdvanced Text Analysis Techniques

Cited by 587

Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model

Fabbri, A., Li, I., She, T., Li, S., & Radev, D. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingAdvanced Text Analysis Techniques

Cited by 449

Crosslingual Generalization through Multitask Finetuning

Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 361

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 193

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

Zhong, M., Yin, D., Yu, T., Zaidi, A., Mutuma, M., Jha, R., Awadallah, A. H., Celikyilmaz, A., Liu, Y., Qiu, X., et al. (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 178

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, S., Sanh, V., Yong, Z. X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Natural Language Processing TechniquesTopic Modeling

Cited by 178

Data Governance in the Age of Large-Scale Data-Driven Language Technology

Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Johnson, I., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 75

Predicting the impact of scientific concepts using full-text features

McKeown, K., Daume, H., Chaturvedi, S., Paparrizos, J., Thadani, K., Barrio, P., Biran, O., Bothe, S., Collins, M., Fleischmann, K. R., et al. (2016) · Journal of the Association for Information Science and Technology

scientometrics and bibliometrics researchAdvanced Text Analysis Techniques

Cited by 73

Binding Language Models in Symbolic Languages

Cheng, Z., Xie, T., Shi, P., Li, C., Nadkarni, R., Hu, Y., Xiong, C., Radev, D., Ostendorf, M., Zettlemoyer, L., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 53

LEVER: Learning to Verify Language-to-Code Generation with Execution

Ni, A., Iyer, S., Radev, D., Stoyanov, V., Yih, W.-T., Wang, S. I., & Lin, X. V. (2023) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 46

SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents

Zhang, Y., Ni, A., Mao, Z., Wu, C. H., Zhu, C., Deb, B., Awadallah, A., Radev, D., & Zhang, R. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Advanced Text Analysis TechniquesTopic Modeling

Cited by 44

CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning

Tang, X., Nair, A., Wang, B., Wang, B., Desai, J., Wade, A., Li, H., Celikyilmaz, A., Mehdad, Y., & Radev, D. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 43

FOLIO: Natural Language Reasoning with First-Order Logic

Han, S., Schoelkopf, H., Zhao, Y., Qi, Z., Riddell, M., Zhou, W., Coady, J., Peng, D., Qiao, Y., Benson, L., et al. (2022) · arXiv

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 35

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

Yong, Z. X., Schoelkopf, H., Muennighoff, N., Aji, A. F., Adelani, D. I., Almubarak, K., Bari, M. S., Sutawika, L., Kasai, J., Baruwa, A., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 30

Crosslingual Generalization through Multitask Finetuning

Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Scao, T. L., Bari, M. S., Shen, S., Yong, Z.-X., Schoelkopf, H., et al. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 29

Overview and Results: CL-SciSumm Shared Task 2019

Chandrasekaran, M. K., Yasunaga, M., Radev, D., Freitag, D., & Kan, M.-Y. (2019) · arXiv

Advanced Text Analysis TechniquesTopic Modeling

Cited by 27

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, S. H., Sanh, V., Yong, Z.-X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 21

Surveyor: A System for Generating Coherent Survey Articles for Scientific Topics

Jha, R., Coke, R., & Radev, D. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Text Analysis TechniquesTopic Modeling

Cited by 19

Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study

Yang, R., Zeng, Q., You, K., Qiao, Y., Huang, L., Hsieh, C.-C., Rosand, B., Goldwasser, J., Dave, A., Keenan, T., et al. (2024) · Journal of Medical Internet Research

Topic ModelingNatural Language Processing Techniques

Cited by 16

Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies

Nan, L., Zhao, Y., Zou, W., Ri, N., Tae, J., Zhang, E., Cohan, A., & Radev, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

Yong, Z.-X., Schoelkopf, H., Muennighoff, N., Aji, A. F., Adelani, D. I., Almubarak, K., Bari, M. S., Sutawika, L., Kasai, J., Baruwa, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2