Drawing causal conclusions from observational real-world data is a very much
desired but challenging task. In this paper we present mixed-method analyses to
investigate causal influences of publication trends and behavior on the
adoption, persistence, and retirement of certain research foci --
methodologies, materials, and tasks that are of interest to the computational
linguistics (CL) community. Our key findings highlight evidence of the
transition to rapidly emerging methodologies in the research community (e.g.,
adoption of bidirectional LSTMs influencing the retirement of LSTMs), the
persistent engagement with trending tasks and techniques (e.g., deep learning,
embeddings, generative, and language models), the effect of scientist location
from outside the US, e.g., China on propensity of researching languages beyond
English, and the potential impact of funding for large-scale research programs.
We anticipate this work to provide useful insights about publication trends and
behavior and raise the awareness about the potential for causal inference in
the computational linguistics and a broader scientific community.

本文利用混合方法分析了互联网上关于计算语言学的出版趋势、行为对研究聚焦点 —— 方法、材料和任务 —— 的采用、持久和退役的因果影响，发现了快速新方法 —— 双向 LSTMs 的引入影响了 LSTMs 的退役，深度学习，嵌入，生成和语言模型等趋势任务和技术在研究社区中持续受关注，并发现科学家所在地（中国等）和大规模研究项目的资金可能会对超出英语之外的语言进行研究的倾向产生影响。这项工作为计算语言学和更广泛的科学社区提供了有用的见解，提高了关于因果推断的潜力的认识。