A Report on the 2020 Sarcasm Detection Shared Task
This work addresses the need for standardized evaluation in sarcasm detection, which is crucial for sentiment analysis, but it is incremental as it focuses on benchmarking rather than introducing new methods.
The paper reports on a shared task for sarcasm detection conducted at the FigLang 2020 workshop, aiming to benchmark state-of-the-art methods in natural language processing to analyze and facilitate progress in this area.
Detecting sarcasm and verbal irony is critical for understanding people's actual sentiments and beliefs. Thus, the field of sarcasm analysis has become a popular research problem in natural language processing. As the community working on computational approaches for sarcasm detection is growing, it is imperative to conduct benchmarking studies to analyze the current state-of-the-art, facilitating progress in this area. We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang 2020) at ACL 2020.