4 citations · 4 across the 3 of their papers we have counts for
5 papers
A Report on the 2020 Sarcasm Detection Shared Task
Debanjan Ghosh, Avijit Vajpayee, Smaranda Muresan
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 i…
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan +2
Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument min…
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking
Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi +4
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extra…
: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan +1
We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major charac…
The Role of Conversation Context for Sarcasm Detection in Online Interactions
Debanjan Ghosh, Alexander Richard Fabbri, Smaranda Muresan
Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker's sarcastic intent is not always obvious without additional…