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Found 35 papers, 5 papers with code
Chandler: An Explainable Sarcastic Response Generator
We introduce Chandler, a system that generates sarcastic responses to a given utterance.
SMASH at Qur’an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering Scenarios
The Qur’an QA 2022 shared task aims at assessing the possibility of building systems that can extract answers to religious questions given relevant passages from the Holy Qur’an.
Embedding Structured Dictionary Entries
Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space.
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
Most of the participating teams utilised pre-trained language models.
Should a Chatbot be Sarcastic? Understanding User Preferences Towards Sarcasm Generation
Previous sarcasm generation research has focused on how to generate text that people perceive as sarcastic to create more human-like interactions.
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic
This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic.
Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm Detection
The introduction of transformer-based language models has been a revolutionary step for natural language processing (NLP) research.
Emoji and Self-Identity in Twitter Bios
Emoji are widely used to express emotions and concepts on social media, and prior work has shown that users’ choice of emoji reflects the way that they wish to present themselves to the world.
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis
Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health.
Don't Take it Personally: Analyzing Gender and Age Differences in Ratings of Online Humor
Computational humor detection systems rarely model the subjectivity of humor responses, or consider alternative reactions to humor - namely offense.
SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense
Our subtasks were binary humor detection, prediction of humor and offense ratings, and a novel controversy task: to predict if the variance in the humor ratings was higher than a specific threshold.
Black or White but never neutral: How readers perceive identity from yellow or skin-toned emoji
Research in sociology and linguistics shows that people use language not only to express their own identity but to understand the identity of others.
Identity Signals in Emoji Do not Influence Perception of Factual Truth on Twitter
Prior work has shown that Twitter users use skin-toned emoji as an act of self-representation to express their racial/ethnic identity.
Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating
The use of pre-trained language models such as BERT and ULMFiT has become increasingly popular in shared tasks, due to their powerful language modelling capabilities.
Examining the Role of Mood Patterns in Predicting Self-Reported Depressive symptoms
Depression is the leading cause of disability worldwide.
Stance Detection on Social Media: State of the Art and Trends
In addition, this study explores the emerging trends and different applications of stance detection on social media.
Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity
However, it is unclear exactly how activity on this platform relates to larger conversations happening elsewhere on the web, such as discussions on larger, more popular social media platforms.
Urban Dictionary Embeddings for Slang NLP Applications
The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models.
Multitask Learning for Arabic Offensive Language and Hate-Speech Detection
Offensive language and hate-speech are phenomena that spread with the rising popularity of social media.
Overview of OSACT4 Arabic Offensive Language Detection Shared Task
This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4).
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset
Our analysis shows the highly subjective nature of these tasks, which is demonstrated by the shift in sentiment labels based on annotators{'} biases.
The Effect of Sociocultural Variables on Sarcasm Communication Online
In this paper we fill this gap by performing a quantitative analysis on the influence of sociocultural variables, including gender, age, country, and English language nativeness, on the effectiveness of sarcastic communication online.
SemEval-2016 Task 3: Community Question Answering
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic.
SemEval-2015 Task 3: Answer Selection in Community Question Answering
Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e. g., the exploitation of the interaction between users and the structure of related posts.
iSarcasm: A Dataset of Intended Sarcasm
We consider the distinction between intended and perceived sarcasm in the context of textual sarcasm detection.
Exploring Author Context for Detecting Intended vs Perceived Sarcasm
We investigate the impact of using author context on textual sarcasm detection.
Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media
Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like.
Assessing Sentiment of the Expressed Stance on Social Media
Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or opposing.
Mazajak: An Online Arabic Sentiment Analyser
Sentiment analysis (SA) is one of the most useful natural language processing applications.
Ranked #1 on
Sentiment Analysis
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Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations
Speech acts are the actions that a speaker intends when performing an utterance within conversations.
Abusive Language Detection on Arabic Social Media
We expand the list of obscene words using this classification, and we report results on a newly created dataset of classified Arabic tweets (obscene, offensive, and clean).
