Mahsa Shafaei


2024

pdf
Labeling Comic Mischief Content in Online Videos with a Multimodal Hierarchical-Cross-Attention Model
Elaheh Baharlouei | Mahsa Shafaei | Yigeng Zhang | Hugo Jair Escalante | Thamar Solorio
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

We address the challenge of detecting questionable content in online media, specifically the subcategory of comic mischief. This type of content combines elements such as violence, adult content, or sarcasm with humor, making it difficult to detect. Employing a multimodal approach is vital to capture the subtle details inherent in comic mischief content. To tackle this problem, we propose a novel end-to-end multimodal system for the task of comic mischief detection. As part of this contribution, we release a novel dataset for the targeted task consisting of three modalities: video, text (video captions and subtitles), and audio. We also design a HIerarchical Cross-attention model with CAPtions (HICCAP) to capture the intricate relationships among these modalities. The results show that the proposed approach makes a significant improvement over robust baselines and state-of-the-art models for comic mischief detection and its type classification. This emphasizes the potential of our system to empower users, to make informed decisions about the online content they choose to see.

pdf
Positive and Risky Message Assessment for Music Products
Yigeng Zhang | Mahsa Shafaei | Fabio Gonzalez | Thamar Solorio
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

In this work, we introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products. We initiate by setting a multi-faceted, multi-task benchmark for music content assessment. Subsequently, we introduce an efficient multi-task predictive model fortified with ordinality-enforcement to address this challenge. Our findings reveal that the proposed method not only significantly outperforms robust task-specific alternatives but also possesses the capability to assess multiple aspects simultaneously. Furthermore, through detailed case studies, where we employed Large Language Models (LLMs) as surrogates for content assessment, we provide valuable insights to inform and guide future research on this topic. The code for dataset creation and model implementation is publicly available at https://github.com/RiTUAL-UH/music-message-assessment.

2021

pdf
From None to Severe: Predicting Severity in Movie Scripts
Yigeng Zhang | Mahsa Shafaei | Fabio Gonzalez | Thamar Solorio
Findings of the Association for Computational Linguistics: EMNLP 2021

In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The proposed dataset and source code are publicly available at our GitHub repository.

pdf
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Daniel Khashabi | Arman Cohan | Siamak Shakeri | Pedram Hosseini | Pouya Pezeshkpour | Malihe Alikhani | Moin Aminnaseri | Marzieh Bitaab | Faeze Brahman | Sarik Ghazarian | Mozhdeh Gheini | Arman Kabiri | Rabeeh Karimi Mahabagdi | Omid Memarrast | Ahmadreza Mosallanezhad | Erfan Noury | Shahab Raji | Mohammad Sadegh Rasooli | Sepideh Sadeghi | Erfan Sadeqi Azer | Niloofar Safi Samghabadi | Mahsa Shafaei | Saber Sheybani | Ali Tazarv | Yadollah Yaghoobzadeh
Transactions of the Association for Computational Linguistics, Volume 9

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1

pdf
A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers
Mahsa Shafaei | Christos Smailis | Ioannis Kakadiaris | Thamar Solorio
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers. First, we introduce a new dataset containing videos of movie trailers in English downloaded from IMDB and YouTube, along with their corresponding age-suitability rating labels. Secondly, we propose a multi-modal deep learning pipeline addressing the movie trailer age suitability rating problem. This is the first attempt to combine video, audio, and speech information for this problem, and our experimental results show that multi-modal approaches significantly outperform the best mono and bimodal models in this task.

2020

pdf
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues
Mahsa Shafaei | Niloofar Safi Samghabadi | Sudipta Kar | Thamar Solorio
Proceedings of the Twelfth Language Resources and Evaluation Conference

Movies help us learn and inspire societal change. But they can also contain objectionable content that negatively affects viewers’ behaviour, especially children. In this paper, our goal is to predict the suitability of movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We create a corpus for movie MPAA ratings and propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 81% weighted F1-score for the classification model that outperforms the traditional machine learning method by 7%.

pdf
Attending the Emotions to Detect Online Abusive Language
Niloofar Safi Samghabadi | Afsheen Hatami | Mahsa Shafaei | Sudipta Kar | Thamar Solorio
Proceedings of the Fourth Workshop on Online Abuse and Harms

In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus for the task of abusive language detection that is collected from a semi-anonymous online platform, and unlike the majority of other available resources, is not created based on a specific list of bad words. We also develop computational models to incorporate emotions into textual cues to improve aggression identification. We evaluate our proposed methods on a set of corpora related to the task and show promising results with respect to abusive language detection.