Papers by Sabit Hassan
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias (2023.findings-acl)
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| Challenge: | Infusing clustering with active learning with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL’s annotation efficiency. |
| Approach: | They propose an algorithm that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate. |
| Outcome: | The proposed algorithm outperforms baseline AL approaches with pretrained transformers and traditional Support Vector Machines on eight datasets for emotion, hatespeech, dialog act, and book type detection tasks. |
Cross-lingual Emotion Detection (2022.lrec-1)
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| Challenge: | Emotion detection is a useful tool for understanding human behavior, but constructing annotated datasets to train models can be expensive. |
| Approach: | They propose to use English as the source language with Arabic and Spanish as target languages to train models for emotion detection in a target language. |
| Outcome: | The proposed approaches surpass state-of-the-art models in Arabic and Spanish by 4% and 5% respectively. |
ASAD: Arabic Social media Analytics and unDerstanding (2021.eacl-demos)
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| Challenge: | Currently, there are no publicly available tools for analyzing Arabic social media, such as ADIDA and CAMeL, which are not trained with Twitter data. |
| Approach: | They propose to use Arabic social media analysis and unDerstanding to analyze tweets using a web API and a user interface. |
| Outcome: | The proposed system allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets. |
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination (2022.lrec-1)
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| Challenge: | Social media are integrated with our daily life and are used to circulate information. |
| Approach: | They develop and publicly release the first largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign. |
| Outcome: | The proposed dataset is the largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign, covering many countries in the Arab region. |
APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations (2022.coling-1)
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| Challenge: | Using style-transfer models to reduce offensiveness of social media comments is difficult because of limited labeled data. |
| Approach: | They propose two methods to integrate discourse relations with pretrained style-transfer models and evaluate them on a reddit dataset. |
| Outcome: | The proposed models can reduce offensiveness while preserving original meaning . they are the first to examine inferential links between comment and original text . |
Multilingual Content Moderation: A Case Study on Reddit (2023.eacl-main)
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| Challenge: | a growing need for AI moderators to safeguard users and protect mental health of human moderator from traumatic content. |
| Approach: | They propose to use a multilingual dataset to study the challenges of content moderation . they propose to analyze 1.8 million Reddit comments in English, german, spanish and french . |
| Outcome: | The proposed dataset highlights the challenges and suggests related research problems . it shows that the proposed model can be used to predict the violated rule . |
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI (2025.coling-industry)
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Yuya Asano, Sabit Hassan, Paras Sharma, Anthony B. Sicilia, Katherine Atwell, Diane Litman, Malihe Alikhani
| Challenge: | Existing ASR correction methods rely on prior user data or named entities . Existing methods based on prior data are not available for goal-oriented dialogues . |
| Approach: | They propose a method that integrates contextual information from the dialogue states of a goal-oriented conversational AI and its tasks into a large language model. |
| Outcome: | The proposed method improves recall and F1 of correction by 34% and 16% while maintaining precision and false positive rate. |
Modeling Intensification for Sign Language Generation: A Computational Approach (2022.findings-acl)
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| Challenge: | End-to-end sign language generation models do not accurately represent prosody in sign language. |
| Approach: | They propose to model intensification in a data-driven manner to improve prosody in generated sign languages by modeling temporal and spatial variations. |
| Outcome: | The proposed models improve the prosody of generated sign languages by using data-driven models. |
MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations (2023.findings-acl)
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| Challenge: | Literature suggests that actively engaged patients are more likely to obtain the full benefits of an intervention and exhibit better outcomes. |
| Approach: | They propose to annotate a dataset of patient-nurse conversations about cancer symptom management using a new framework for patient engagement. |
| Outcome: | The proposed model predicts patient-nurse conversations from socio-affective and cognitive dimensions. |
An Active Learning Framework for Inclusive Generation by Large Language Models (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) exhibit bias toward underrepresented groups, despite advances in active learning. |
| Approach: | They propose a clustering-based active learning framework enhanced with knowledge distillation that transforms the intermediate outputs of the learner model to yield more representative models without prior knowledge of underlying data distribution. |
| Outcome: | The proposed framework improves performance across data subgroups and lexical diversity, underscoring the model’s resilience to skewness in available data. |