Search Results for author: Walid Magdy

Found 39 papers, 9 papers with code

Should a Chatbot be Sarcastic? Understanding User Preferences Towards Sarcasm Generation

no code implementations ACL 2022 Silviu Vlad Oprea, Steven Wilson, Walid Magdy

Previous sarcasm generation research has focused on how to generate text that people perceive as sarcastic to create more human-like interactions.

Chatbot

Embedding Structured Dictionary Entries

no code implementations EMNLP (insights) 2020 Steven Wilson, Walid Magdy, Barbara McGillivray, Gareth Tyson

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.

Learning Word Embeddings Multi-Task Learning

Emoji and Self-Identity in Twitter Bios

no code implementations EMNLP (NLP+CSS) 2020 Jinhang Li, Giorgos Longinos, Steven Wilson, Walid Magdy

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.

Chandler: An Explainable Sarcastic Response Generator

no code implementations EMNLP (ACL) 2021 Silviu Oprea, Steven Wilson, Walid Magdy

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

1 code implementation OSACT (LREC) 2022 Amr Keleg, Walid Magdy

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.

Language Modelling Question Answering

Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm Detection

no code implementations EACL (WANLP) 2021 Ibrahim Abu Farha, Walid Magdy

The introduction of transformer-based language models has been a revolutionary step for natural language processing (NLP) research.

Benchmarking Sarcasm Detection

Estimating the Level of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasets

1 code implementation18 May 2024 Amr Keleg, Walid Magdy, Sharon Goldwater

On annotating multi-dialect Arabic datasets, it is common to randomly assign the samples across a pool of native Arabic speakers.

Sentence Sentence Classification

ALDi: Quantifying the Arabic Level of Dialectness of Text

1 code implementation20 Oct 2023 Amr Keleg, Sharon Goldwater, Walid Magdy

Transcribed speech and user-generated text in Arabic typically contain a mixture of Modern Standard Arabic (MSA), the standardized language taught in schools, and Dialectal Arabic (DA), used in daily communications.

Dialect Identification Sentence

Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification

1 code implementation20 Oct 2023 Amr Keleg, Walid Magdy

Automatic Arabic Dialect Identification (ADI) of text has gained great popularity since it was introduced in the early 2010s.

Dialect Identification Multi-Label Classification

DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language Models

1 code implementation8 Jun 2023 Amr Keleg, Walid Magdy

A new benchmark DLAMA-v1 is built of factual triples from three pairs of contrasting cultures having a total of 78, 259 triples from 20 relation predicates.

Benchmarking Fairness

Don't Take it Personally: Analyzing Gender and Age Differences in Ratings of Online Humor

no code implementations23 Aug 2022 J. A. Meaney, Steven R. Wilson, Luis Chiruzzo, Walid Magdy

Computational humor detection systems rarely model the subjectivity of humor responses, or consider alternative reactions to humor - namely offense.

Humor Detection

SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense

no code implementations SEMEVAL 2021 J. A. Meaney, Steven Wilson, Luis Chiruzzo, Adam Lopez, Walid Magdy

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.

Humor Detection

Black or White but never neutral: How readers perceive identity from yellow or skin-toned emoji

no code implementations12 May 2021 Alexander Robertson, Walid Magdy, Sharon Goldwater

Research in sociology and linguistics shows that people use language not only to express their own identity but to understand the identity of others.

Sociology

Identity Signals in Emoji Do not Influence Perception of Factual Truth on Twitter

no code implementations7 May 2021 Alexander Robertson, Walid Magdy, Sharon Goldwater

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

no code implementations SEMEVAL 2020 J. A. Meaney, Steven Wilson, Walid Magdy

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.

Classification Language Modelling +1

Stance Detection on Social Media: State of the Art and Trends

no code implementations5 Jun 2020 Abeer Al-Dayel, Walid Magdy

In addition, this study explores the emerging trends and different applications of stance detection on social media.

Opinion Mining Sentiment Analysis +1

Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity

no code implementations15 May 2020 Steven R. Wilson, Walid Magdy, Barbara McGillivray, Gareth Tyson

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

no code implementations LREC 2020 Steven Wilson, Walid Magdy, Barbara McGillivray, Kiran Garimella, Gareth Tyson

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.

Clustering Sarcasm Detection +4

From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset

no code implementations LREC 2020 Ibrahim Abu Farha, Walid Magdy

Our analysis shows the highly subjective nature of these tasks, which is demonstrated by the shift in sentiment labels based on annotators{'} biases.

Arabic Sentiment Analysis Sarcasm Detection

Overview of OSACT4 Arabic Offensive Language Detection Shared Task

no code implementations LREC 2020 Hamdy Mubarak, Kareem Darwish, Walid Magdy, Tamer Elsayed, Hend Al-Khalifa

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).

The Effect of Sociocultural Variables on Sarcasm Communication Online

no code implementations10 Apr 2020 Silviu Vlad Oprea, Walid Magdy

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-2015 Task 3: Answer Selection in Community Question Answering

no code implementations SEMEVAL 2015 Preslav Nakov, Lluís Màrquez, Walid Magdy, Alessandro Moschitti, James Glass, Bilal Randeree

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.

Answer Selection Community Question Answering

iSarcasm: A Dataset of Intended Sarcasm

no code implementations ACL 2020 Silviu Oprea, Walid Magdy

We consider the distinction between intended and perceived sarcasm in the context of textual sarcasm detection.

Sarcasm Detection

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

1 code implementation8 Aug 2019 Abeer Aldayel, Walid Magdy

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.

Stance Detection

Assessing Sentiment of the Expressed Stance on Social Media

no code implementations8 Aug 2019 Abeer Aldayel, Walid Magdy

Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or opposing.

Stance Detection

Abusive Language Detection on Arabic Social Media

no code implementations WS 2017 Hamdy Mubarak, Kareem Darwish, Walid Magdy

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).

Abusive Language General Classification

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