DeepAI
Log In Sign Up

UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection using generative-based and mutation-based data augmentation

04/18/2022
by   Amirhossein Abaskohi, et al.
0

Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of sarcasm presents a significant difficulty for sentiment analysis systems based on affective computing. The methodology and results of our team, UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in this paper. We put different models, and data augmentation approaches to the test and report on which one works best. The tests begin with traditional machine learning models and progress to transformer-based and attention-based models. We employed data augmentation based on data mutation and data generation. Using RoBERTa and mutation-based data augmentation, our best approach achieved an F1-sarcastic of 0.38 in the competition's evaluation phase. After the competition, we fixed our model's flaws and achieved an F1-sarcastic of 0.414.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/05/2020

Enhanced Offensive Language Detection Through Data Augmentation

Detecting offensive language on social media is an important task. The I...
03/29/2021

Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysis

Data augmentation is a way to increase the diversity of available data b...
07/14/2020

Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR

Recently Deep Transformer models have proven to be particularly powerful...
09/25/2019

Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis

In this article we describe our participation in TASS 2019, a shared tas...
03/13/2022

On Data Augmentation in Point Process Models Based on Thinning

Many models for point process data are defined through a thinning proced...
10/11/2022

T5 for Hate Speech, Augmented Data and Ensemble

We conduct relatively extensive investigations of automatic hate speech ...