Exploring Author Context for Detecting Intended vs Perceived Sarcasm

ACL 2019

Exploring Author Context for Detecting Intended vs Perceived Sarcasm

Jan 30, 2021
|
25 views
|
Details
Abstract: We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling. Authors: Silviu Oprea, Walid Magdy (University of Edinburgh)

Comments
loading...