Roth, Dan

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2024) · ACM Computing Surveys

Topic ModelingNatural Language Processing Techniques

Cited by 1,387

Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey

Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heinz, I., & Roth, D. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,387

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Yin, W., Hay, J., & Roth, D. (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 ModelingDomain Adaptation and Few-Shot Learning

Cited by 537

Solving General Arithmetic Word Problems

Roy, S., & Roth, D. (2016) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 275

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., & Berant, J. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 242

“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding

Zhou, B., Khashabi, D., Ning, Q., & Roth, D. (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 137

Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims

Chen, S., Khashabi, D., Yin, W., Callison-Burch, C., & Roth, D. (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 ModelingSentiment Analysis and Opinion Mining

Cited by 104

Extending Multilingual BERT to Low-Resource Languages

Wang, Z., K, K., Mayhew, S., & Roth, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Topic ModelingNatural Language Processing Techniques

Cited by 90

Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection

Chen, S., Zhang, F., Sone, K., & Roth, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 83

Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary

Deutsch, D., Bedrax-Weiss, T., & Roth, D. (2021) · Transactions of the Association for Computational Linguistics

Advanced Text Analysis TechniquesTopic Modeling

Cited by 75

Rethinking with Retrieval: Faithful Large Language Model Inference

He, H., Zhang, H., & Roth, D. (2023) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 50

ReCode: Robustness Evaluation of Code Generation Models

Wang, S., Li, Z., Qian, H., Yang, C., Wang, Z., Shang, M., Kumar, V., Tan, S., Ray, B., Bhatia, P., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Software Engineering ResearchTopic Modeling

Cited by 42

Multi-lingual Evaluation of Code Generation Models

Athiwaratkun, B., Gouda, S. K., Wang, Z., Li, X., Tian, Y., Tan, M., Ahmad, W. U., Wang, S., Sun, Q., Shang, M., et al. (2022) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 38

Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval

Alon, U., Xu, F. F., He, J., Sengupta, S., Roth, D., & Neubig, G. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 32

On the Limitations of Reference-Free Evaluations of Generated Text

Deutsch, D., Dror, R., & Roth, D. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 29

ExpertQA: Expert-Curated Questions and Attributed Answers

Malaviya, C., Lee, S., Chen, S., Sieber, E., Yatskar, M., & Roth, D. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Expert finding and Q&A systemsTopic Modeling

Cited by 28

Do Language Embeddings capture Scales?

Zhang, X., Ramachandran, D., Tenney, I., Elazar, Y., & Roth, D. (2020) · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Topic ModelingMultimodal Machine Learning Applications

Cited by 22

Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics

Deutsch, D., Dror, R., & Roth, D. (2022) · arXiv

Advanced Text Analysis TechniquesNatural Language Processing Techniques

Cited by 22

Contextualized Evaluations: Judging Language Model Responses to Underspecified Queries

Malaviya, C., Chang, J. C., Roth, D., Iyyer, M., Yatskar, M., & Lo, K. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 4

ExpertQA: Expert-Curated Questions and Attributed Answers

Malaviya, C., Lee, S., Chen, S., Sieber, E., Yatskar, M., & Roth, D. (2023) · arXiv

Topic ModelingExpert finding and Q&A systems

Cited by 2

A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

Jiang, B., Xie, Y., Hao, Z., Wang, X., Mallick, T., Su, W. J., Taylor, C. J., & Roth, D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach

Zhang, J., Zhang, H., Deng, Z., & Roth, D. (2022) · arXiv

Ethics and Social Impacts of AIscientometrics and bibliometrics research

Cited by 1