At NEURIPS 2023, New Orleans, at the WANT workshop, presenting my work on DYAD - a blocksparse, GPU-aware approximation to the MLP Layer. ⟶
In the past few years, I have also been involved in co-organizing many collaborative NLP research efforts, such as:
The GEM benchmark, associated workshop@ACL'21, and paper for better and standardized evaluation and comparison of NLG models and systems - a parallel to GLUE for generation
The challenge sets submodule of GEM, where we built domain-shifted sets under a unified theme for NLG tasks in our benchmark, using various perturbation [backtranslation], sub-selection [length] and other domain shift [diachronic] strategies. Our work was accepted @
NEURIPS'21 Datasets & Benchmarks Track!
The NL-Augmenter participative repository and benchmark, which provides a structure for NLPers to contribute and evaluate task-specific data augmentations a.k.a transformations, as well as subset selection strategies a.k.a filters. We aim to create a large, usable suite (~140 and counting!) of transformations and filters leveraging wisdom-of-the-crowd - opening the door to more systematic analysis and deployment of data augmentation/robustness evaluation.
