papers

2023

1. EMNLP Findings

   Energy and Carbon Considerations of Fine-Tuning BERT

   Xiaorong Wang*, Clara Na*, Emma Strubell, Sorelle Friedler, and Sasha Luccioni

   In Findings of the Association for Computational Linguistics: EMNLP 2023 2023

   arXiv Bib PDF

   @inproceedings{wang2023energy,
     title = {Energy and Carbon Considerations of Fine-Tuning BERT},
     author = {Wang, Xiaorong and Na, Clara and Strubell, Emma and Friedler, Sorelle and Luccioni, Sasha},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
     abs = {},
     year = {2023},
     eprint = {2311.10267},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
     equal_contribution = {2},
     abbr = {EMNLP Findings},
     pdf = {energy_and_carbon.pdf},
     selected = true,
     arxiv = {2311.10267},
     bibtex_show = true
   }

2. EMNLP Main

   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, and Emma Strubell

   In The 2023 Conference on Empirical Methods in Natural Language Processing 2023

   arXiv Bib PDF

   @inproceedings{gururaja2023build,
     title = {To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing},
     author = {Gururaja, Sireesh and Bertsch, Amanda and Na, Clara and Widder, David Gray and Strubell, Emma},
     year = {2023},
     eprint = {2310.07715},
     archiveprefix = {arXiv},
     primaryclass = {cs.CL},
     selected = true,
     booktitle = {The 2023 Conference on Empirical Methods in Natural Language Processing},
     abbr = {EMNLP Main},
     pdf = {to_build_our_future.pdf},
     bibtex_show = true,
     arxiv = {2310.07715},
     equal_contribution = {3}
   }

3. EMNLP Main

   The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment

   Jared Fernandez, Jacob Kahn, Clara Na, Yonatan Bisk, and Emma Strubell

   In The 2023 Conference on Empirical Methods in Natural Language Processing 2023

   arXiv Bib PDF

   @inproceedings{fernandez2023framework,
     title = {The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment},
     author = {Fernandez, Jared and Kahn, Jacob and Na, Clara and Bisk, Yonatan and Strubell, Emma},
     year = {2023},
     eprint = {2302.06117},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
     abbr = {EMNLP Main},
     selected = {true},
     booktitle = {The 2023 Conference on Empirical Methods in Natural Language Processing},
     arxiv = {2302.06117},
     pdf = {the_framework_tax.pdf},
     bibtex_show = true,
     equal_contribution = {0}
   }

2022

1. EMNLP Findings

   Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models

   Clara Na, Sanket Vaibhav Mehta, and Emma Strubell

   In Findings of the Association for Computational Linguistics: EMNLP 2022 Dec 2022

   arXiv Bib HTML PDF Poster

   @inproceedings{na-etal-2022-train,
     title = {Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models},
     author = {Na, Clara and Mehta, Sanket Vaibhav and Strubell, Emma},
     editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2022},
     month = dec,
     year = {2022},
     address = {Abu Dhabi, United Arab Emirates},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2022.findings-emnlp.361},
     doi = {10.18653/v1/2022.findings-emnlp.361},
     pages = {4909--4936},
     abbr = {EMNLP Findings},
     pdf = {train-flat-then-compress.pdf},
     poster = {poster-trainflat.pdf},
     selected = {true},
     arxiv = {2205.12694},
     html = {https://aclanthology.org/2022.findings-emnlp.361},
     bibtex_show = true
   }

2. Virtual Task Selection in Meta-Learning for Domain Generalization in Semantic Parsing

   Cathy Jiao*, Xuecong (Cindy) Fu*, Clara Na*, and Emma Strubell

   2022

   PDF

2020

1. Less Is More? In Patents, Design Transformations that Add Occur More Often Than Those that Subtract

   Katelyn Stenger, Clara Na, and Leidy Klotz

   In Design Computing and Cognition’20 2020

   Abs Bib HTML

   This article examines how design transformations are described in one specific but important context: patents. Using text analytics, we examined term frequency and term frequency-inverse document frequency from 33,100 full patents from 2017 sourced from the US Patent and Trade Office. Using a corpus-based approach, we developed lexicons to capture two general types of design transformation: addition and subtraction. In patent data we collected and analyzed, addition design transformations were more common than subtraction design transformations (2.7:1). The ratio of addition to subtraction was higher than ratios in non-design texts (1:2.5). While patents represent one area of design, and the patent texts we analyzed were not necessarily written by designers themselves, something about the process that produces patents leads to far greater use of addition than subtraction. We discuss possible reasons for and implications of these findings.

   @inproceedings{10.1007/978-3-030-90625-2_16,
     author = {Stenger, Katelyn and Na, Clara and Klotz, Leidy},
     editor = {Gero, John S.},
     title = {Less Is More? In Patents, Design Transformations that Add Occur More Often Than Those that Subtract},
     booktitle = {Design Computing and Cognition'20},
     year = {2020},
     publisher = {Springer International Publishing},
     address = {Cham},
     pages = {283--295},
     isbn = {978-3-030-90625-2},
     html = {https://link.springer.com/chapter/10.1007/978-3-030-90625-2_16},
     bibtex_show = true,
     equal_contribution = {0}
   }