Jesse Dodge | DeepAI

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∙ 07/19/2023

Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation

Rising computational demands of modern natural language processing (NLP)...

0 Hao Peng, et al. ∙

∙ 06/29/2023

Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research

Many recent improvements in NLP stem from the development and use of lar...

0 Ji-Ung Lee, et al. ∙

∙ 06/16/2023

Reproducibility in NLP: What Have We Learned from the Checklist?

Scientific progress in NLP rests on the reproducibility of researchers' ...

0 Ian Magnusson, et al. ∙

∙ 06/09/2023

Evaluating the Social Impact of Generative AI Systems in Systems and Society

Generative AI systems across modalities, ranging from text, image, audio...

2 Irene Solaiman, et al. ∙

∙ 06/03/2023

Stubborn Lexical Bias in Data and Models

In NLP, recent work has seen increased focus on spurious correlations be...

0 Sofia Serrano, et al. ∙

∙ 02/14/2023

AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models

Pretrained language models (PLMs) are trained on massive corpora, but of...

0 Alexandra Chronopoulou, et al. ∙

∙ 12/19/2022

Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications

Scholarly text is often laden with jargon, or specialized language that ...

0 Li Lucy, et al. ∙

∙ 06/13/2022

Modeling the Machine Learning Multiverse

Amid mounting concern about the reliability and credibility of machine l...

0 Samuel J. Bell, et al. ∙

∙ 06/10/2022

Measuring the Carbon Intensity of AI in Cloud Instances

By providing unprecedented access to computational resources, cloud comp...

0 Jesse Dodge, et al. ∙

∙ 05/04/2022

Data Governance in the Age of Large-Scale Data-Driven Language Technology

The recent emergence and adoption of Machine Learning technology, and sp...

0 Yacine Jernite, et al. ∙

∙ 03/11/2022

Staged Training for Transformer Language Models

The current standard approach to scaling transformer language models tra...

8 Sheng Shen, et al. ∙

∙ 12/16/2021

Efficient Hierarchical Domain Adaptation for Pretrained Language Models

Generative language models are trained on diverse, general domain corpor...

0 Alexandra Chronopoulou, et al. ∙

∙ 10/01/2021

Expected Validation Performance and Estimation of a Random Variable's Maximum

Research in NLP is often supported by experimental results, and improved...

0 Jesse Dodge, et al. ∙

∙ 04/18/2021

Documenting the English Colossal Clean Crawled Corpus

As language models are trained on ever more text, researchers are turnin...

7 Jesse Dodge, et al. ∙

∙ 04/17/2021

Competency Problems: On Finding and Removing Artifacts in Language Data

Much recent work in NLP has documented dataset artifacts, bias, and spur...

0 Matt Gardner, et al. ∙

∙ 04/16/2020

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

As NLP models become larger, executing a trained model requires signific...

0 Roy Schwartz, et al. ∙

∙ 02/15/2020

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

Fine-tuning pretrained contextual word embedding models to supervised do...

0 Jesse Dodge, et al. ∙

∙ 09/06/2019

RNN Architecture Learning with Sparse Regularization

Neural models for NLP typically use large numbers of parameters to reach...

13 Jesse Dodge, et al. ∙

∙ 09/06/2019

Show Your Work: Improved Reporting of Experimental Results

Research in natural language processing proceeds, in part, by demonstrat...

26 Jesse Dodge, et al. ∙

∙ 07/22/2019

Green AI

The computations required for deep learning research have been doubling ...

4 Roy Schwartz, et al. ∙

∙ 06/06/2017

Random Search for Hyperparameters using Determinantal Point Processes

We propose the use of k-determinantal point processes in hyperparameter ...

0 Jesse Dodge, et al. ∙

∙ 06/09/2016

Key-Value Memory Networks for Directly Reading Documents

Directly reading documents and being able to answer questions from them ...

0 Alexander Miller, et al. ∙

∙ 11/21/2015

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

A long-term goal of machine learning is to build intelligent conversatio...

0 Jesse Dodge, et al. ∙

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