@inproceedings{landes-etal-2023-deepzensols,
title = "{D}eep{Z}ensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility",
author = "Landes, Paul and
Di Eugenio, Barbara and
Caragea, Cornelia",
editor = "Tan, Liling and
Milajevs, Dmitrijs and
Chauhan, Geeticka and
Gwinnup, Jeremy and
Rippeth, Elijah",
booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlposs-1.16",
doi = "10.18653/v1/2023.nlposs-1.16",
pages = "141--146",
abstract = "Given the criticality and difficulty of reproducing machine learning experiments, there have been significant efforts in reducing the variance of these results. The ability to consistently reproduce results effectively strengthens the underlying hypothesis of the work and should be regarded as important as the novel aspect of the research itself. The contribution of this work is an open source framework that has the following characteristics: a) facilitates reproducing consistent results, b) allows hot-swapping features and embeddings without further processing and re-vectorizing the dataset, c) provides a means of easily creating, training and evaluating natural language processing deep learning models with little to no code changes, and d) is freely available to the community.",
}
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%0 Conference Proceedings
%T DeepZensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility
%A Landes, Paul
%A Di Eugenio, Barbara
%A Caragea, Cornelia
%Y Tan, Liling
%Y Milajevs, Dmitrijs
%Y Chauhan, Geeticka
%Y Gwinnup, Jeremy
%Y Rippeth, Elijah
%S Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F landes-etal-2023-deepzensols
%X Given the criticality and difficulty of reproducing machine learning experiments, there have been significant efforts in reducing the variance of these results. The ability to consistently reproduce results effectively strengthens the underlying hypothesis of the work and should be regarded as important as the novel aspect of the research itself. The contribution of this work is an open source framework that has the following characteristics: a) facilitates reproducing consistent results, b) allows hot-swapping features and embeddings without further processing and re-vectorizing the dataset, c) provides a means of easily creating, training and evaluating natural language processing deep learning models with little to no code changes, and d) is freely available to the community.
%R 10.18653/v1/2023.nlposs-1.16
%U https://aclanthology.org/2023.nlposs-1.16
%U https://doi.org/10.18653/v1/2023.nlposs-1.16
%P 141-146
Markdown (Informal)
[DeepZensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility](https://aclanthology.org/2023.nlposs-1.16) (Landes et al., NLPOSS-WS 2023)
ACL