Training for Fast Sequential Prediction Using Dynamic Feature Selection. - Emma Strubell; Luke Vilnis; Andrew McCallum (CoRR, 2014)

Learning Dynamic Feature Selection for Fast Sequential Prediction. - Emma Strubell; Luke Vilnis; Kate Silverstein; Andrew McCallum (ACL (1), 2015)

Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches. - Benjamin Roth 001; Nicholas Monath; David Belanger 002; Emma Strubell; Patrick Verga; Andrew McCallum (TAC, 2015)

Multilingual Relation Extraction using Compositional Universal Schema. - Patrick Verga; David Belanger 002; Emma Strubell; Benjamin Roth 001; Andrew McCallum (HLT-NAACL, 2016)

Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema. - Haw-Shiuan Chang; Abdurrahman Munir; Ao Liu 003; Johnny Tian-Zheng Wei; Aaron Traylor; Ajay Nagesh; Nicholas Monath; Patrick Verga; Emma Strubell; Andrew McCallum (TAC, 2016)

Attending to All Mention Pairs for Full Abstract Biological Relation Extraction. - Patrick Verga; Emma Strubell; Ofer Shai; Andrew McCallum (AKBC@NIPS, 2017)

Dependency Parsing with Dilated Iterated Graph CNNs. - Emma Strubell; Andrew McCallum (SPNLP@EMNLP, 2017)

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions. - Emma Strubell; Patrick Verga; David Belanger 002; Andrew McCallum (EMNLP, 2017)

Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions. - Emma Strubell; Patrick Verga; David Belanger 002; Andrew McCallum (CoRR, 2017)

Automatically Extracting Action Graphs from Materials Science Synthesis Procedures. - Sheshera Mysore; Edward Kim 004; Emma Strubell; Ao Liu 003; Haw-Shiuan Chang; Srikrishna Kompella; Kevin Huang 004; Andrew McCallum; Elsa A. Olivetti (CoRR, 2017)

Multi-Task Learning For Parsing The Alexa Meaning Representation Language. - Vittorio Perera; Tagyoung Chung; Thomas Kollar; Emma Strubell (AAAI, 2018)

Linguistically-Informed Self-Attention for Semantic Role Labeling. - Emma Strubell; Patrick Verga; Daniel Andor; David Weiss 001; Andrew McCallum (EMNLP, 2018)

Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction. - Patrick Verga; Emma Strubell; Andrew McCallum (NAACL-HLT, 2018)

Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL? - Emma Strubell; Andrew McCallum (CoRR, 2018)

Energy and Policy Considerations for Deep Learning in NLP. - Emma Strubell; Ananya Ganesh; Andrew McCallum (ACL (1), 2019)

The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures. - Sheshera Mysore; Zach Jensen; Edward Kim 004; Kevin Huang 004; Haw-Shiuan Chang; Emma Strubell; Jeffrey Flanigan; Andrew McCallum; Elsa A. Olivetti (LAW@ACL, 2019)

Energy and Policy Considerations for Modern Deep Learning Research. - Emma Strubell; Ananya Ganesh; Andrew McCallum (AAAI, 2020)

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. - Edward Kim 004; Zach Jensen; Alexander van Grootel; Kevin Huang 004; Matthew Staib; Sheshera Mysore; Haw-Shiuan Chang; Emma Strubell; Andrew McCallum; Stefanie Jegelka; Elsa A. Olivetti (J. Chem. Inf. Model., 2020)

Comparing Span Extraction Methods for Semantic Role Labeling. - Zhisong Zhang; Emma Strubell; Eduard H. Hovy (SPNLP@ACL-IJCNLP, 2021)

WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensing. - Amee Trivedi; Kate Silverstein; Emma Strubell; Prashant J. Shenoy; Mohit Iyyer (COMPASS, 2021)

On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling. - Zhisong Zhang; Emma Strubell; Eduard H. Hovy (EMNLP (1), 2021)

WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensing. - Amee Trivedi; Kate Silverstein; Emma Strubell; Prashant J. Shenoy (CoRR, 2021)

Improving Compositional Generalization with Self-Training for Data-to-Text Generation. - Sanket Vaibhav Mehta; Jinfeng Rao; Yi Tay; Mihir Kale; Ankur Parikh; Hongtao Zhong; Emma Strubell (CoRR, 2021)

Improving Compositional Generalization with Self-Training for Data-to-Text Generation. - Sanket Vaibhav Mehta; Jinfeng Rao; Yi Tay; Mihir Kale; Ankur Parikh; Emma Strubell (ACL (1), 2022)

Bridging Fairness and Environmental Sustainability in Natural Language Processing. - Marius Hessenthaler; Emma Strubell; Dirk Hovy; Anne Lauscher (EMNLP, 2022)

Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models. - Clara Na; Sanket Vaibhav Mehta; Emma Strubell (EMNLP (Findings), 2022)

Transfer Learning from Semantic Role Labeling to Event Argument Extraction with Template-based Slot Querying. - Zhisong Zhang; Emma Strubell; Eduard H. Hovy (EMNLP, 2022)

A Survey of Active Learning for Natural Language Processing. - Zhisong Zhang; Emma Strubell; Eduard H. Hovy (EMNLP, 2022)

Measuring the Carbon Intensity of AI in Cloud Instances. - Jesse Dodge; Taylor Prewitt; Remi Tachet des Combes; Erika Odmark; Roy Schwartz 001; Emma Strubell; Alexandra Sasha Luccioni; Noah A. Smith; Nicole DeCario; Will Buchanan (FAccT, 2022)

SQuAT: Sharpness- and Quantization-Aware Training for BERT. - Zheng Wang; Juncheng B. Li; Shuhui Qu; Florian Metze; Emma Strubell (CoRR, 2022)

Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution. - Nupoor Gandhi; Anjalie Field; Emma Strubell (CoRR, 2022)

Error-aware Quantization through Noise Tempering. - Zheng Wang; Juncheng B. Li; Shuhui Qu; Florian Metze; Emma Strubell (CoRR, 2022)

Efficient and Equitable Natural Language Processing in the Age of Deep Learning (Dagstuhl Seminar 22232). - Jesse Dodge; Iryna Gurevych; Roy Schwartz 001; Emma Strubell; Betty van Aken (Dagstuhl Reports, 2022)

To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering. - Dheeru Dua; Emma Strubell; Sameer Singh 001; Pat Verga (ACL (1), 2023)

Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution. - Nupoor Gandhi; Anjalie Field; Emma Strubell (ACL (1), 2023)

The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment. - Jared Fernandez; Jacob Kahn; Clara Na; Yonatan Bisk; Emma Strubell (EMNLP, 2023)

Understanding the Effect of Model Compression on Social Bias in Large Language Models. - Gustavo Gonçalves; Emma Strubell (EMNLP, 2023)

To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing. - Sireesh Gururaja; Amanda Bertsch; Clara Na; David Gray Widder; Emma Strubell (EMNLP, 2023)

DSI++: Updating Transformer Memory with New Documents. - Sanket Vaibhav Mehta; Jai Gupta 001; Yi Tay; Mostafa Dehghani 001; Vinh Q. Tran 002; Jinfeng Rao; Marc Najork; Emma Strubell; Donald Metzler (EMNLP, 2023)

Energy and Carbon Considerations of Fine-Tuning BERT. - Xiaorong Wang; Clara Na; Emma Strubell; Sorelle A. Friedler; Sasha Luccioni (EMNLP (Findings), 2023)

Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training. - Zhisong Zhang; Emma Strubell; Eduard H. Hovy (EMNLP (Findings), 2023)

Making Scalable Meta Learning Practical. - Sang Keun Choe; Sanket Vaibhav Mehta; Hwijeen Ahn; Willie Neiswanger; Pengtao Xie; Emma Strubell; Eric P. Xing (NeurIPS, 2023)

On the Interactions of Structural Constraints and Data Resources for Structured Prediction. - Zhisong Zhang; Emma Strubell; Eduard H. Hovy (SustaiNLP, 2023)

The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment. - Jared Fernandez; Jacob Kahn; Clara Na; Yonatan Bisk; Emma Strubell (CoRR, 2023)

Regularizing Self-training for Unsupervised Domain Adaptation via Structural Constraints. - Rajshekhar Das; Jonathan Francis; Sanket Vaibhav Mehta; Jean Oh; Emma Strubell; José M. F. Moura (CoRR, 2023)

Large Language Model Distillation Doesn't Need a Teacher. - Ananya Harsh Jha; Dirk Groeneveld; Emma Strubell; Iz Beltagy (CoRR, 2023)

Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research. - Ji-Ung Lee; Haritz Puerto; Betty van Aken; Yuki Arase; Jessica Zosa Forde; Leon Derczynski; Andreas Rücklé; Iryna Gurevych; Roy Schwartz 001; Emma Strubell; Jesse Dodge (CoRR, 2023)

Queer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Models. - Harnoor Dhingra; Preetiha Jayashanker; Sayali Moghe; Emma Strubell (CoRR, 2023)

Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation. - Hao Peng 009; Qingqing Cao; Jesse Dodge; Matthew E. Peters; Jared Fernandez; Tom Sherborne; Kyle Lo; Sam Skjonsberg; Emma Strubell; Darrell Plessas; Iz Beltagy; Evan Pete Walsh; et al. (CoRR, 2023)

An Empirical Investigation of the Role of Pre-training in Lifelong Learning. - Sanket Vaibhav Mehta; Darshan Patil; Sarath Chandar; Emma Strubell (J. Mach. Learn. Res., 2023)

Efficient Methods for Natural Language Processing: A Survey. - Marcos V. Treviso; Ji-Ung Lee; Tianchu Ji; Betty van Aken; Qingqing Cao; Manuel R. Ciosici; Michael Hassid; Kenneth Heafield; Sara Hooker; Colin Raffel; Pedro Henrique Martins; André F. T. Martins; et al. (Trans. Assoc. Comput. Linguistics, 2023)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Evan Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (ACL (1), 2024)

AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters. - Li Lucy; Suchin Gururangan; Luca Soldaini; Emma Strubell; David Bamman; Lauren F. Klein; Jesse Dodge (ACL (1), 2024)

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research. - Luca Soldaini; Rodney Kinney; Akshita Bhagia; Dustin Schwenk; David Atkinson; Russell Authur; Ben Bogin; Khyathi Raghavi Chandu; Jennifer Dumas; Yanai Elazar; Valentin Hofmann; Ananya Harsh Jha; et al. (ACL (1), 2024)

Gradient Localization Improves Lifelong Pretraining of Language Models. - Jared Fernandez; Yonatan Bisk; Emma Strubell (EMNLP (Findings), 2024)

Scalable Data Ablation Approximations for Language Models through Modular Training and Merging. - Clara Na; Ian Magnusson; Ananya Harsh Jha; Tom Sherborne; Emma Strubell; Jesse Dodge; Pradeep Dasigi (EMNLP, 2024)

Power Hungry Processing: Watts Driving the Cost of AI Deployment? - Sasha Luccioni; Yacine Jernite; Emma Strubell (FAccT, 2024)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (CoRR, 2024)

Source-Aware Training Enables Knowledge Attribution in Language Models. - Muhammad Khalifa; David Wadden; Emma Strubell; Honglak Lee; Lu Wang 008; Iz Beltagy; Hao Peng 009 (CoRR, 2024)

Carbon Connect: An Ecosystem for Sustainable Computing. - Benjamin C. Lee; David Brooks 001; Arthur van Benthem; Udit Gupta; Gage Hills; Vincent Liu 001; Benjamin Pierce; Christopher Stewart; Emma Strubell; Gu-Yeon Wei; Adam Wierman; Yuan Yao 006; et al. (CoRR, 2024)

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. - Sang Keun Choe; Hwijeen Ahn; Juhan Bae; Kewen Zhao; Minsoo Kang; Youngseog Chung; Adithya Pratapa; Willie Neiswanger; Emma Strubell; Teruko Mitamura; Jeff G. Schneider; Eduard H. Hovy; et al. (CoRR, 2024)

Stereotype or Personalization? User Identity Biases Chatbot Recommendations. - Anjali Kantharuban; Jeremiah Milbauer; Emma Strubell; Graham Neubig (CoRR, 2024)

Collage: Decomposable Rapid Prototyping for Information Extraction on Scientific PDFs. - Sireesh Gururaja; Yueheng Zhang; Guannan Tang; Tianhao Zhang; Kevin Murphy; Yu-Tsen Yi; Junwon Seo; Anthony D. Rollett; Emma Strubell (CoRR, 2024)

Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training. - Jared Fernandez; Luca Wehrstedt; Leonid Shamis; Mostafa Elhoushi; Kalyan Saladi; Yonatan Bisk; Emma Strubell; Jacob Kahn (CoRR, 2024)

Generate to Discriminate: Expert Routing for Continual Learning. - Yewon Byun; Sanket Vaibhav Mehta; Saurabh Garg; Emma Strubell; Michael Oberst; Bryan Wilder; Zachary C. Lipton (CoRR, 2024)

Energy Considerations of Large Language Model Inference and Efficiency Optimizations. - Jared Fernandez; Clara Na; Vashisth Tiwari; Yonatan Bisk; Sasha Luccioni; Emma Strubell (ACL (1), 2025)

Beyond Text: Characterizing Domain Expert Needs in Document Research. - Sireesh Gururaja; Nupoor Gandhi; Jeremiah Milbauer; Emma Strubell (ACL (Findings), 2025)

Stereotype or Personalization? User Identity Biases Chatbot Recommendations. - Anjali Kantharuban; Jeremiah Milbauer; Maarten Sap; Emma Strubell; Graham Neubig (ACL (Findings), 2025)

From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate. - Alexandra Sasha Luccioni; Emma Strubell; Kate Crawford 002 (FAccT, 2025)

Holistically Evaluating the Environmental Impact of Creating Language Models. - Jacob Morrison; Clara Na; Jared Fernandez; Tim Dettmers; Emma Strubell; Jesse Dodge (ICLR, 2025)

A view of the sustainable computing landscape. - Benjamin C. Lee; David Brooks 001; Arthur van Benthem; Mariam Elgamal; Udit Gupta; Gage Hills; Vincent Liu 001; Linh Thi Xuan Phan; Benjamin Pierce; Christopher Stewart; Emma Strubell; Gu-Yeon Wei; et al. (Patterns, 2025)

Efficient Hardware Scaling and Diminishing Returns in Large-Scale Training of Language Models. - Jared Fernandez; Luca Wehrstedt; Leonid Shamis; Mostafa Elhoushi; Kalyan Saladi; Yonatan Bisk; Emma Strubell; Jacob Kahn (Trans. Mach. Learn. Res., 2025)

International AI Safety Report. - Yoshua Bengio; Sören Mindermann; Daniel Privitera; Tamay Besiroglu; Rishi Bommasani; Stephen Casper; Yejin Choi 001; Philip Fox; Ben Garfinkel; Danielle Goldfarb; Hoda Heidari; Anson Ho; et al. (CoRR, 2025)

Structured Extraction of Process Structure Properties Relationships in Materials Science. - Amit K. Verma; Zhisong Zhang; Junwon Seo; Robin Kuo; Runbo Jiang; Emma Strubell; Anthony D. Rollett (CoRR, 2025)

On-device Streaming Discrete Speech Units. - Kwanghee Choi; Masao Someki; Emma Strubell; Shinji Watanabe 001 (CoRR, 2025)

Kinetics: Rethinking Test-Time Scaling Laws. - Ranajoy Sadhukhan; Zhuoming Chen; Haizhong Zheng; Yang Zhou; Emma Strubell; Beidi Chen (CoRR, 2025)

Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation. - Alex C. Newkirk; Jared Fernandez; Jonathan Koomey; Imran Latif; Emma Strubell; Arman Shehabi; Constantine Samaras (CoRR, 2025)

Misinformation by Omission: The Need for More Environmental Transparency in AI. - Sasha Luccioni; Boris Gamazaychikov; Théo Alves Da Costa; Emma Strubell (CoRR, 2025)