Pith. sign in

REVIEW 1 cited by

A Report on the 2020 Sarcasm Detection Shared Task

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2005.05814 v2 pith:4D3E54AL submitted 2020-05-12 cs.CL cs.LG

Debanjan Ghosh , Avijit Vajpayee , Smaranda Muresan This is my paper · ORCID
classification cs.CLcs.LG
keywords sarcasmdetectionlanguageprocessingreportsharedtaskactual
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Token-free Models for Sarcasm Detection

    cs.CL 2025-05 conditional novelty 4.0 of 10

    ByT5-small reaches 89.87% and CANINE reaches 72.88% on news-headline and Twitter sarcasm detection, each edging a T5 baseline by less than one accuracy point.

Pith tools