no code implementations • 11 Jul 2023 • Dhruv Mullick, Bilal Ghanem, Alona Fyshe
Customer feedback is invaluable to companies as they refine their products.
no code implementations • 10 Apr 2023 • Bilal Ghanem, Alona Fyshe
At the same time, DISTO ranks the performance of state-of-the-art DG models very differently from MT-based metrics, showing that MT metrics should not be used for distractor evaluation.
no code implementations • 6 Jun 2022 • Dhruv Mullick, Alona Fyshe, Bilal Ghanem
Aspect-based Sentiment Analysis (ABSA) helps to explain customers' opinions towards products and services.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)
no code implementations • Findings (ACL) 2022 • Bilal Ghanem, Lauren Lutz Coleman, Julia Rivard Dexter, Spencer McIntosh von der Ohe, Alona Fyshe
We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.
no code implementations • ACL 2021 • Sanja Stajner, Seren Yenikent, Bilal Ghanem, Marc Franco-Salvador
According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people{'}s everyday life and career.
1 code implementation • EACL (WANLP) 2021 • Hala Mulki, Bilal Ghanem
Moreover, Let-Mi was used as an evaluation dataset through binary/multi-/target classification tasks conducted by several state-of-the-art machine learning systems along with Multi-Task Learning (MTL) configuration.
1 code implementation • EACL 2021 • Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso, Francisco Rangel
To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture.
1 code implementation • 6 Feb 2020 • Bilal Ghanem, Jihen Karoui, Farah Benamara, Paolo Rosso, Véronique Moriceau
This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system.
no code implementations • 15 Oct 2019 • Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso
We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features.
no code implementations • 3 Oct 2019 • Bilal Ghanem, Davide Buscaldi, Paolo Rosso
Our approach is mainly based on textual features which utilize thematic information, and profiling features to identify the accounts from their way of writing tweets.
no code implementations • 26 Aug 2019 • Bilal Ghanem, Paolo Rosso, Francisco Rangel
Fake news is risky since it has been created to manipulate the readers' opinions and beliefs.
no code implementations • SEMEVAL 2019 • Bilal Ghanem, Aless Cignarella, ra Teresa, Cristina Bosco, Paolo Rosso, Francisco Manuel Rangel Pardo
In the present paper we describe the UPV-28-UNITO system{'}s submission to the RumorEval 2019 shared task.
no code implementations • WS 2018 • Bilal Ghanem, Paolo Rosso, Francisco Rangel
Furthermore, we have investigated the importance of different lexicons in the detection of the classification labels.
no code implementations • SEMEVAL 2018 • Bilal Ghanem, Francisco Rangel, Paolo Rosso
In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection.