Gurevych, Iryna

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reimers, N., & Gurevych, I. (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 ModelingSentiment Analysis and Opinion Mining

Cited by 12,778

AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., & Gurevych, I. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 636

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

Reimers, N., & Gurevych, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 463

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reimers, N., & Gurevych, I. (2019) · arXiv

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 343

Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference

Falke, T., Ribeiro, L. F. R., Utama, P. A., Dagan, I., & Gurevych, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 265

How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models

Rust, P., Pfeiffer, J., Vulić, I., Ruder, S., & Gurevych, I. (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)

Natural Language Processing TechniquesTopic Modeling

Cited by 202

Classification and Clustering of Arguments with Contextualized Word Embeddings

Reimers, N., Schiller, B., Beck, T., Daxenberger, J., Stab, C., & Gurevych, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 157

Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning

Sailer, M., Bauer, E., Hofmann, R., Kiesewetter, J., Glas, J., Gurevych, I., & Fischer, F. (2023) · Learning and Instruction

Intelligent Tutoring Systems and Adaptive LearningStudent Assessment and Feedback

Cited by 106

A Survey of Confidence Estimation and Calibration in Large Language Models . -

Geng, J., Cai, F., Wang, Y., Koeppl, H., Nakov, P., & Gurevych, I. (2024) · Proceedings of the 2024 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 88

NLPeer: A Unified Resource for the Computational Study of Peer Review

Dycke, N., Kuznetsov, I., & Gurevych, I. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Publishing and Scholarly CommunicationAcademic Writing and Publishing

Cited by 50

Does My Rebuttal Matter? Insights from a Major NLP Conference

Gao, Y., Eger, S., Kuznetsov, I., Gurevych, I., & Miyao, Y. (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)

Natural Language Processing TechniquesTopic Modeling

Cited by 48

BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Thakur, N., Reimers, N., Rücklé, A., Srivastava, A., & Gurevych, I. (2021) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 45

C4Corpus: Multilingual Web-size Corpus with Free License

Habernal, I., Zayed, O., & Gurevych, I. (2016) · Proceedings of the Language Resources and Evaluation Conference

Natural Language Processing TechniquesWeb Data Mining and Analysis

Cited by 30

Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches

Reimers, N., & Gurevych, I. (2018) · arXiv

Machine Learning and Data ClassificationTopic Modeling

Cited by 30

M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection

Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., Tsvigun, A., Mohammed Afzal, O., Mahmoud, T., Puccetti, G., Arnold, T., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 25

Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review

Kuznetsov, I., Buchmann, J., Eichler, M., & Gurevych, I. (2022) · Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 17

Are Emergent Abilities in Large Language Models just In-Context Learning?

Lu, S., Bigoulaeva, I., Sachdeva, R., Madabushi, H. T., & Gurevych, I. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12

IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators

Paul, I., Glavaš, G., & Gurevych, I. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 12

Yes-Yes-Yes: Proactive Data Collection for ACL Rolling Review and Beyond

Dycke, N., Kuznetsov, I., & Gurevych, I. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Ethics and Social Impacts of AIComputational and Text Analysis Methods

Cited by 10

LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection

Abassy, M., Elozeiri, K., Aziz, A., Ta, M. N., Tomar, R. V., Adhikari, B., Ahmed, S. E. D., Wang, Y., Mohammed Afzal, O., Xie, Z., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 7

Elastic Weight Removal for Faithful and Abstractive Dialogue Generation

Daheim, N., Dziri, N., Sachan, M., Gurevych, I., & Ponti, E. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Speech and dialogue systemsTopic Modeling

Cited by 7

What Can Natural Language Processing Do for Peer Review?

Kuznetsov, I., Afzal, O. M., Dercksen, K., Dycke, N., Goldberg, A., Hope, T., Hovy, D., Kummerfeld, J. K., Lauscher, A., Leyton-Brown, K., et al. (2024) · arXiv

Academic Writing and PublishingPublishing and Scholarly Communication

Cited by 6

Assisting Decision Making in Scholarly Peer Review: A Preference Learning Perspective

Dycke, N., Simpson, E., Kuznetsov, I., & Gurevych, I. (2021) · arXiv

Recommender Systems and TechniquesGame Theory and Voting Systems

Cited by 3

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers

Wang, Y., Reddy, R. G., Mujahid, Z. M., Arora, A., Rubashevskii, A., Geng, J., Afzal, O. M., Pan, L., Borenstein, N., Pillai, A., et al. (2023) · arXiv

Misinformation and Its ImpactsTopic Modeling

Cited by 3