2022 EMNLP EMNLP 2022

A Major Obstacle for NLP Research: Let’s Talk about Time Allocation!

Abstract

AbstractThe 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary
🧭 Keyword Pioneer — time allocation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio