232 citations · 300 across the 7 of their papers we have counts for
8 papers
Resolving the Human Subjects Status of Machine Learning's Crowdworkers
Divyansh Kaushik, Zachary C. Lipton, Alex John London
In recent years, machine learning (ML) has relied heavily on crowdworkers both for building datasets and for addressing research questions requiring human interaction or judgment.…
Practical Benefits of Feature Feedback Under Distribution Shift
Anurag Katakkar, Clay H. Yoo, Weiqin Wang +2
In attempts to develop sample-efficient and interpretable algorithms, researcher have explored myriad mechanisms for collecting and exploiting feature feedback (or rationales) auxi…
On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study
Divyansh Kaushik, Douwe Kiela, Zachary C. Lipton +1
In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that…
Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie +16
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop datase…
Explaining The Efficacy of Counterfactually Augmented Data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy +1
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…
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
Divyansh Kaushik, Eduard Hovy, Zachary C. Lipton
Despite alarm over the reliance of machine learning systems on so-called spurious patterns, the term lacks coherent meaning in standard statistical frameworks. However, the languag…
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Yifan Wu, Ezra Winston, Divyansh Kaushik +1
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domai…
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks
Divyansh Kaushik, Zachary C. Lipton
Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and…