Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community

TL;DR

This study investigates causal factors influencing publication trends and researcher behavior in computational linguistics, revealing how methodological shifts, geographic factors, and funding impact research focus and community dynamics.

Contribution

It introduces mixed-method causal analysis to understand publication trends and researcher behavior in computational linguistics, highlighting key influences like methodology adoption and geographic factors.

Findings

Emergence of new methodologies like bidirectional LSTMs influences retirement of older models

Persistent engagement with trending tasks such as deep learning and language models

External factors like researcher location and funding impact research focus

Abstract

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.