greenai

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce "Data Maps"---a model-based tool to characterize and diagnose datasets. We …

The Right Tool for the Job: Matching Model and Instance Complexities

As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference budget, we propose a modification to contextual representation …

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Fine-tuning pretrained contextual word embedding models to supervised downstream tasks has become commonplace in natural language processing. This process, however, is often brittle: even with the same hyperparameter values, distinct random seeds can …

Green AI

The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically, deep learning …

RNN Architecture Learning with Sparse Regularization

Neural models for NLP typically use large numbers of parameters to reach state-of-the- art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient …

Show Your Work: Improved Reporting of Experimental Results

Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance …