Amal Alabdulkarim


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Guiding Neural Story Generation with Reader Models
Xiangyu Peng | Kaige Xie | Amal Alabdulkarim | Harshith Kayam | Samihan Dani | Mark Riedl
Findings of the Association for Computational Linguistics: EMNLP 2022

Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-topictoward a specific ending when generating narratives with neural language models. In this paper, we introduce Story generation with ReaderModels (StoRM), a framework in which areader model is used to reason about the storyshould progress. A reader model infers whata human reader believes about the concepts,entities, and relations about the fictional storyworld. We show how an explicit reader modelrepresented as a knowledge graph affords the storycoherence and provides controllability in theform of achieving a given story world stategoal. Experiments show that our model produces significantly more coherent and on-topicstories, outperforming baselines in dimensionsincluding plot plausibility and staying on topic


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Automatic Story Generation: Challenges and Attempts
Amal Alabdulkarim | Siyan Li | Xiangyu Peng
Proceedings of the Third Workshop on Narrative Understanding

Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language.

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AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking
Tariq Alhindi | Amal Alabdulkarim | Ali Alshehri | Muhammad Abdul-Mageed | Preslav Nakov
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages. One task of interest is claim veracity prediction, which can be addressed using stance detection with respect to relevant documents retrieved online. To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claim–article pairs from a diverse set of sources comprising three fact-checking websites and one news website. AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is well-balanced between related and unrelated documents with respect to the claims. We benchmark AraStance, along with two other stance detection datasets, using a number of BERT-based models. Our best model achieves an accuracy of 85% and a macro F1 score of 78%, which leaves room for improvement and reflects the challenging nature of AraStance and the task of stance detection in general.


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Spider-Jerusalem at SemEval-2019 Task 4: Hyperpartisan News Detection
Amal Alabdulkarim | Tariq Alhindi
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system for detecting hyperpartisan news articles, which was submitted for the shared task in SemEval 2019 on Hyperpartisan News Detection. We developed a Support Vector Machine (SVM) model that uses TF-IDF of tokens, Language Inquiry and Word Count (LIWC) features, and structural features such as number of paragraphs and hyperlink count in an article. The model was trained on 645 articles from two classes: mainstream and hyperpartisan. Our system was ranked seventeenth out of forty two participating teams in the binary classification task with an accuracy score of 0.742 on the blind test set (the accuracy of the top ranked system was 0.822). We provide a detailed description of our preprocessing steps, discussion of our experiments using different combinations of features, and analysis of our results and prediction errors.