Das, Dipanjan

BERT Rediscovers the Classical NLP Pipeline

Tenney, I., Das, D., & Pavlick, E. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,307

BLEURT: Learning Robust Metrics for Text Generation

Sellam, T., Das, D., & Parikh, A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,005

BLEURT: Learning Robust Metrics for Text Generation

Sellam, T., Das, D., & Parikh, A. P. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,005

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

What do you learn from context? Probing for sentence structure in contextualized word representations

Tenney, I., Xia, P., Chen, B., Wang, A., Poliak, A., McCoy, R. T., Kim, N., Van Durme, B., Bowman, S. R., Das, D., et al. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 698

ToTTo: A Controlled Table-To-Text Generation Dataset

Parikh, A., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingMultimodal Machine Learning Applications

Cited by 252

ToTTo: A Controlled Table-To-Text Generation Dataset

Parikh, A. P., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 252

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

Natural Language Processing TechniquesTopic Modeling

Cited by 159

Handling Divergent Reference Texts when Evaluating Table-to-Text Generation

Dhingra, B., Faruqui, M., Parikh, A., Chang, M.-W., Das, D., & Cohen, W. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 150

Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features

Rashkin, H., Reitter, D., Tomar, G. S., & Das, 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)

Speech and dialogue systemsTopic Modeling

Cited by 74

Measuring Attribution in Natural Language Generation Models

Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2023) · Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 71

Language Models are Multilingual Chain-of-Thought Reasoners

Shi, F., Suzgun, M., Freitag, M., Wang, X., Srivats, S., Vosoughi, S., Chung, H. W., Tay, Y., Ruder, S., Zhou, D., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 60

Decontextualization: Making Sentences Stand-Alone

Choi, E., Palomaki, J., Lamm, M., Kwiatkowski, T., Das, D., & Collins, M. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 54

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Anuoluwapo, A., Bosselut, A., Chandu, K. R., Clinciu, M., Das, D., Dhole, K. D., et al. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 49

The MultiBERTs: BERT Reproductions for Robustness Analysis

Sellam, T., Yadlowsky, S., Wei, J., Saphra, N., D'Amour, A., Linzen, T., Bastings, J., Turc, I., Eisenstein, J., Das, D., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 37

Measuring Attribution in Natural Language Generation Models

Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 34

Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Bohnet, B., Tran, V. Q., Verga, P., Aharoni, R., Andor, D., Soares, L. B., Ciaramita, M., Eisenstein, J., Ganchev, K., Herzig, J., et al. (2022) · arXiv

Topic ModelingAuthorship Attribution and Profiling

Cited by 30

Conditional Generation with a Question-Answering Blueprint

Narayan, S., Maynez, J., Amplayo, R. K., Ganchev, K., Louis, A., Huot, F., Sandholm, A., Das, D., & Lapata, M. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 24

SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge

Haas, L., Yona, G., D'Antonio, G., Goldshtein, S., & Das, D. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents

Gupta, N., Chatterjee, R., Haas, L., Tao, C., Wang, A., Liu, C., Oiwa, H., Gribovskaya, E., Ackermann, J., Blitzer, J., et al. (2026) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 0