Text-Based Causal Inference on Irony and Sarcasm Detection


Çekinel R. F. , Karagöz P.

24th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2022, Vienna, Austria, 22 - 24 August 2022, vol.13428 LNCS, pp.31-45 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 13428 LNCS
  • Doi Number: 10.1007/978-3-031-12670-3_3
  • City: Vienna
  • Country: Austria
  • Page Numbers: pp.31-45
  • Keywords: Causal inference, Clustering, Irony detection, Topic modeling
  • Middle East Technical University Affiliated: Yes

Abstract

© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.The state-of-the-art NLP models’ success advanced significantly as their complexity increased in recent years. However, these models tend to consider the statistical correlation between features which may lead to bias. Therefore, to build robust systems, causality should be considered while estimating the given task’s data generating process. In this study, we explore text-based causal inference on the irony and sarcasm detection problem. Additionally, we model the latent confounders by performing unsupervised data analysis, particularly clustering and topic modeling. The obtained results also provide insight for the causal explainability in irony detection.