> Keidar, Daphna, et al. "Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang." Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2022, pp. 1422–42. ACLWeb, <https://doi.org/10.18653/v1/2022.acl-long.101>. # Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang > Languages are continuously undergoing changes, and the mechanisms that underlie these changes are still a matter of debate. In this work, we approach language evolution through the lens of causality in order to model not only how various distributional factors associate with language change, but how they causally affect it. In particular, we study slang, which is an informal language that is typically restricted to a specific group or social setting. We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words. With causal discovery and causal inference techniques, we measure the effect that word type (slang/nonslang) has on both semantic change and frequency shift, as well as its relationship to frequency, polysemy and part of speech. Our analysis provides some new insights in the study of language change, e.g., we show that slang words undergo less semantic change but tend to have larger frequency shifts over time. ## Contributions 1. We formalize the analysis of change dynamics in language with a causal framework 2. We propose a semantic change metric that builds upon contextualized word representations 3. We discover interesting insights about slang words and semantic change,with slang words exhibiting both more rapid frequency fluctuations and less semantic change. ## Causal methodology for change dynamics - *Causal discovery* is the task of uncovering the causal DAG that explains observed data. Assuming a causal DAG, the task of *causal inference* then concerns determining the effect that intervening on a variable (treatment) will have on another variable (outcome). ## More Feder et al. 2021 Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond