no code implementations • 8 Jun 2022 • Divyansh Kaushik, Zachary C. Lipton, Alex John London
We highlight two challenges posed by ML: the same set of workers can serve multiple roles and provide many sorts of information; and ML research tends to embrace a dynamic workflow, where research questions are seldom stated ex ante and data sharing opens the door for future studies to aim questions at different targets.
no code implementations • 14 Oct 2021 • Anurag Katakkar, Clay H. Yoo, Weiqin Wang, Zachary C. Lipton, Divyansh Kaushik
In attempts to develop sample-efficient and interpretable algorithms, researcher have explored myriad mechanisms for collecting and exploiting feature feedback (or rationales) auxiliary annotations provided for training (but not test) instances that highlight salient evidence.
1 code implementation • ACL 2021 • Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton, Wen-tau Yih
In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions.
no code implementations • NAACL 2021 • Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, Adina Williams
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
no code implementations • ICLR 2021 • Divyansh Kaushik, Amrith Setlur, Eduard Hovy, Zachary C. Lipton
In attempts to produce ML models less reliant on spurious patterns in NLP datasets, researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in-the-loop process in which given some documents and their (initial) labels, humans must revise the text to make a counterfactual label applicable.
2 code implementations • ICLR 2020 • Divyansh Kaushik, Eduard Hovy, Zachary C. Lipton
While classifiers trained on either original or manipulated data alone are sensitive to spurious features (e. g., mentions of genre), models trained on the combined data are less sensitive to this signal.
1 code implementation • ICLR Workshop LLD 2019 • Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary Lipton
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.
no code implementations • EMNLP 2018 • Divyansh Kaushik, Zachary C. Lipton
Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples.
no code implementations • RANLP 2017 • Divyansh Kaushik, Shashank Gupta, Chakradhar Raju, Reuben Dias, Sanjib Ghosh
The purpose of this research is to address the problem of extracting information from travel itineraries and discuss the challenges faced in the process.