Bilal Ghanem


Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask
Bilal Ghanem | Lauren Lutz Coleman | Julia Rivard Dexter | Spencer von der Ohe | Alona Fyshe
Findings of the Association for Computational Linguistics: ACL 2022

Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention. Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions. However, many existing Question Generation (QG) systems focus on generating extractive questions from the text, and have no way to control the type of the generated question. In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used. We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill. We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment. Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset. We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.


Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language
Hala Mulki | Bilal Ghanem
Proceedings of the Sixth Arabic Natural Language Processing Workshop

Online misogyny has become an increasing worry for Arab women who experience gender-based online abuse on a daily basis. Misogyny automatic detection systems can assist in the prohibition of anti-women Arabic toxic content. Developing such systems is hindered by the lack of the Arabic misogyny benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset for Arabic misogyny. We further provide a detailed review of the dataset creation and annotation phases. The consistency of the annotations for the proposed dataset was emphasized through inter-rater agreement evaluation measures. 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. The obtained results indicated that the performances achieved by the used systems are consistent with state-of-the-art results for languages other than Arabic, while employing MTL improved the performance of the misogyny/target classification tasks.

What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts
Sanja Stajner | Seren Yenikent | Bilal Ghanem | Marc Franco-Salvador
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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. We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classification task, and as three binary classification tasks. A detailed manual analysis shows that the latter has advantages in the real-world scenario, and that our best models achieve similar performances as a trained human annotator.

FakeFlow: Fake News Detection by Modeling the Flow of Affective Information
Bilal Ghanem | Simone Paolo Ponzetto | Paolo Rosso | Francisco Rangel
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Fake news articles often stir the readers’ attention by means of emotional appeals that arouse their feelings. Unlike in short news texts, authors of longer articles can exploit such affective factors to manipulate readers by adding exaggerations or fabricating events, in order to affect the readers’ emotions. To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture. The proposed model, FakeFlow, learns this flow by combining topic and affective information extracted from text. We evaluate the model’s performance with several experiments on four real-world datasets. The results show that FakeFlow achieves superior results when compared against state-of-the-art methods, thus confirming the importance of capturing the flow of the affective information in news articles.


UPV-28-UNITO at SemEval-2019 Task 7: Exploiting Post’s Nesting and Syntax Information for Rumor Stance Classification
Bilal Ghanem | Alessandra Teresa Cignarella | Cristina Bosco | Paolo Rosso | Francisco Manuel Rangel Pardo
Proceedings of the 13th International Workshop on Semantic Evaluation

In the present paper we describe the UPV-28-UNITO system’s submission to the RumorEval 2019 shared task. The approach we applied for addressing both the subtasks of the contest exploits both classical machine learning algorithms and word embeddings, and it is based on diverse groups of features: stylistic, lexical, emotional, sentiment, meta-structural and Twitter-based. A novel set of features that take advantage of the syntactic information in texts is moreover introduced in the paper.


Stance Detection in Fake News A Combined Feature Representation
Bilal Ghanem | Paolo Rosso | Francisco Rangel
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

With the uncontrolled increasing of fake news and rumors over the Web, different approaches have been proposed to address the problem. In this paper, we present an approach that combines lexical, word embeddings and n-gram features to detect the stance in fake news. Our approach has been tested on the Fake News Challenge (FNC-1) dataset. Given a news title-article pair, the FNC-1 task aims at determining the relevance of the article and the title. Our proposed approach has achieved an accurate result (59.6 % Macro F1) that is close to the state-of-the-art result with 0.013 difference using a simple feature representation. Furthermore, we have investigated the importance of different lexicons in the detection of the classification labels.

LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection
Bilal Ghanem | Francisco Rangel | Paolo Rosso
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection. We have approached the task with our low dimensionality representation method (LDR), which exploits low dimensional features extracted from text on the basis of the occurrence probability of the words depending on each class. Our intuition is that words in ironic texts have different probability of occurrence than in non-ironic ones. Our approach obtained acceptable results in both subtasks A and B. We have performed an error analysis that shows the difference on correct and incorrect classified tweets.