@inproceedings{kann-etal-2022-major,
    title = "A Major Obstacle for {NLP} Research: Let{'}s Talk about Time Allocation!",
    author = "Kann, Katharina  and
      Dudy, Shiran  and
      McCarthy, Arya D.",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/declare-journal/2022.emnlp-main.612/",
    doi = "10.18653/v1/2022.emnlp-main.612",
    pages = "8959--8969",
    abstract = "The field of natural language processing (NLP) has grown over the last few years: conferences have become larger, we have published an incredible amount of papers, and state-of-the-art research has been implemented in a large variety of customer-facing products. However, this paper argues that we have been less successful than we \textit{should} have been and reflects on where and how the field fails to tap its full potential. Specifically, we demonstrate that, in recent years, \textbf{subpar time allocation has been a major obstacle for NLP research}. We outline multiple concrete problems together with their negative consequences and, importantly, suggest remedies to improve the status quo. We hope that this paper will be a starting point for discussions around which common practices are {--} or are \textit{not} {--} beneficial for NLP research."
}