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 *should* have been and reflects on where and how the field fails to tap its full potential. Specifically, we demonstrate that, in recent years, **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 *not* – beneficial for NLP research.- Anthology ID:
- 2022.emnlp-main.612
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8959–8969
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.612
- DOI:
- 10.18653/v1/2022.emnlp-main.612
- Cite (ACL):
- Katharina Kann, Shiran Dudy, and Arya D. McCarthy. 2022. A Major Obstacle for NLP Research: Let’s Talk about Time Allocation!. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8959–8969, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- A Major Obstacle for NLP Research: Let’s Talk about Time Allocation! (Kann et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.612.pdf