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Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
The tremendous amount of user generated data through social networking sites led to the gaining popularity of automatic text classification in the field of computational linguistics over the past decade. …...
Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning
AbstractWe propose a unified multilingual model for humor detection which can be trained under a transfer learning framework. 1) The model is built based on pre-trained multilingual BERT, thereby is …...
CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users
AbstractHumor detection has gained attention in recent years due to the desire to understand user-generated content with figurative language. However, substantial individual and cultural differences in humor perception make it …...
CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense
AbstractHumor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a …...
DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense
AbstractHumor detection and rating poses interesting linguistic challenges to NLP; it is highly subjective depending on the perceptions of a joke and the context in which it is used. This …...
DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense Rating
AbstractThis paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense. In it, we explain the approach used to train …...
Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense
AbstractThis paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection …...
Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness
AbstractHumor and Offense are highly subjective due to multiple word senses, cultural knowledge, and pragmatic competence. Hence, accurately detecting humorous and offensive texts has several compelling use cases in Recommendation …...
RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection
AbstractAn understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared …...
TECHSSN at SemEval-2021 Task 7: Humor and Offense detection and classification using ColBERT embeddings
AbstractThis paper describes the system used for detecting humor in text. The system developed by the team TECHSSN uses binary classification techniques to classify the text. The data undergoes preprocessing …...
UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and Offense
AbstractDetecting humor is a challenging task since words might share multiple valences and, depending on the context, the same words can be even used in offensive expressions. Neural network architectures …...
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting
AbstractIn this paper, we hypothesize that humor is closely related to sentiment and emotions. Also, due to the tremendous growth in multilingual content, there is a great demand for building …...
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and Its Intensity
AbstractPrerecorded laughter accompanying dialog in comedy TV shows encourages the audience to laugh by clearly marking humorous moments in the show. We present an approach for automatically detecting humor in …...