Making Travel Smarter: Extracting Travel Information From Email Itineraries Using Named Entity Recognition. - Divyansh Kaushik; Shashank Gupta; Chakradhar Raju; Reuben Dias; Sanjib Ghosh (RANLP, 2017)

How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks. - Divyansh Kaushik; Zachary C. Lipton (EMNLP, 2018)

Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment. - Yifan Wu; Ezra Winston; Divyansh Kaushik; Zachary C. Lipton (ICML, 2019)

Learning The Difference That Makes A Difference With Counterfactually-Augmented Data. - Divyansh Kaushik; Eduard H. Hovy; Zachary Chase Lipton (ICLR, 2020)

On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study. - Divyansh Kaushik; Douwe Kiela; Zachary C. Lipton; Wen-tau Yih (ACL/IJCNLP (1), 2021)

Explaining the Efficacy of Counterfactually Augmented Data. - Divyansh Kaushik; Amrith Setlur; Eduard H. Hovy; Zachary Chase Lipton (ICLR, 2021)

Dynabench: Rethinking Benchmarking in NLP. - Douwe Kiela; Max Bartolo; Yixin Nie; Divyansh Kaushik; Atticus Geiger; Zhengxuan Wu; Bertie Vidgen; Grusha Prasad; Amanpreet Singh; Pratik Ringshia; Zhiyi Ma; Tristan Thrush; et al. (NAACL-HLT, 2021)

Practical Benefits of Feature Feedback Under Distribution Shift. - Anurag Katakkar; Clay H. Yoo; Weiqin Wang; Zachary C. Lipton; Divyansh Kaushik (BlackboxNLP@EMNLP, 2022)

Resolving the Human Subjects Status of Machine Learning's Crowdworkers. - Divyansh Kaushik; Zachary C. Lipton; Alex John London (CoRR, 2022)

Robustifying NLP with Humans in the Loop - Divyansh Kaushik (2023)

Resolving the Human-subjects Status of Machine Learning's Crowdworkers: What ethical framework should govern the interaction of ML researchers and crowdworkers? - Divyansh Kaushik; Zachary C. Lipton; Alex John London (ACM Queue, 2024)

Resolving the Human-Subjects Status of ML's Crowdworkers. - Divyansh Kaushik; Zachary C. Lipton; Alex John London (Commun. ACM, 2024)