Papers by Isar Nejadgholi
How Does Stereotype Content Differ across Data Sources? (2024.starsem-1)
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| Challenge: | Existing studies of stereotypes using rating scales capture beliefs and opinions about different social groups. |
| Approach: | They compare stereotype-relevant measures of social group social status with traditional scales and a word-list generation task using free-text data. |
| Outcome: | The results compare with traditional surveys and a spontaneous word-list generation task. |
Extracting Age-Related Stereotypes from Social Media Texts (2022.lrec-1)
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| Challenge: | a method for extracting age-related stereotypes from Twitter data is under-studied in NLP . stereotyping on the basis of protected characteristics has been understudied . |
| Approach: | They propose a method for extracting age-related stereotypes from Twitter data . they generate a corpus of 300,000 over-generalizations about four contemporary generations . |
| Outcome: | The method uncovers common stereotypes as reported in media and psychological literature . it also finds that stereotypes for different generations vary across topics . |
Recognizing UMLS Semantic Types with Deep Learning (D19-62)
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Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
| Challenge: | Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. |
| Approach: | They propose to use general and domain-specific information to combine general and specific information to create a new entity recognition method. |
| Outcome: | The proposed method produces a state-of-the-art result on a newly released dataset, MedMentions. |
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Mitigating gender bias in NLP has a long history tied to debiasing static word embeddings. |
| Approach: | They propose a masked language modelling task where content is developed around known social stereotypes and a projective debiasing method is used to reduce bias. |
| Outcome: | The proposed methods reduce intrinsic bias and mitigat observed bias in a downstream setting, but the two outcomes are not necessarily correlated. |
Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification (2022.acl-short)
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| Challenge: | Existing calibration errors are insensitive to large errors in low and mid-range certainty regions. |
| Approach: | They propose a calibration error metric that weights all certainty regions equally. |
| Outcome: | The proposed calibration error metric reduces calibration errors over existing baselines by reducing low and mid certainty estimates. |
Tackling Social Bias against the Poor: a Dataset and a Taxonomy on Aporophobia (2025.findings-naacl)
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Georgina Curto, Svetlana Kiritchenko, Muhammad Hammad Fahim Siddiqui, Isar Nejadgholi, Kathleen C. Fraser
| Challenge: | Poverty is a multidimensional phenomenon that affects 712 million people worldwide . |
| Approach: | They propose to annotate a corpus of English tweets from five world regions for the presence of harmful beliefs and discriminative actions against poor people on social media. |
| Outcome: | The proposed model can be used to identify, track and mitigat aporophobia on social media at scale. |
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection (2022.naacl-main)
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| Challenge: | XAI features usually provide a single importance score for each token, but feature attribution methods provide two complementary and theoretically-grounded scores for each utterance. |
| Approach: | They propose a feature attribution method that generates explicit perturbations of the input text, allowing the importance scores themselves to be explainable. |
| Outcome: | The proposed method explain the predictions of hate speech detection models on a set of curated examples from a test suite. |
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors (2022.acl-long)
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| Challenge: | a new study shows that general abusive language classifiers are reliable in detecting explicit abuse but fail to detect more subtle abuses. |
| Approach: | They propose an interpretability technique to quantify the sensitivity of a trained model to new data . they propose a degree of explicitness metric to suggest out-of-domain unlabeled examples . |
| Outcome: | The proposed interpretability technique is useful for predicting the generalizability of the model on new data. |
Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes (2024.lrec-main)
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| Challenge: | Gender stereotypes are pervasive beliefs about individuals based on their gender that shape societal attitudes, behaviours, and even opportunities. |
| Approach: | They propose eleven strategies to automatically counteract gender stereotypes by generating gender-based counter-stereotypes from a questionnaire to male and female participants. |
| Outcome: | The proposed strategies were perceived as offensive and/or implausible by the raters . humour, perspective-taking, counter-examples, and empathy for the speaker were perceived to be less effective. |
PARME: Parallel Corpora for Low-Resourced Middle Eastern Languages (2025.acl-long)
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Sina Ahmadi, Rico Sennrich, Erfan Karami, Ako Marani, Parviz Fekrazad, Gholamreza Akbarzadeh Baghban, Hanah Hadi, Semko Heidari, Mahîr Dogan, Pedram Asadi, Dashne Bashir, Mohammad Amin Ghodrati, Kourosh Amini, Zeynab Ashourinezhad, Mana Baladi, Farshid Ezzati, Alireza Ghasemifar, Daryoush Hosseinpour, Behrooz Abbaszadeh, Amin Hassanpour, Bahaddin Jalal Hamaamin, Saya Kamal Hama, Ardeshir Mousavi, Sarko Nazir Hussein, Isar Nejadgholi, Mehmet Ölmez, Horam Osmanpour, Rashid Roshan Ramezani, Aryan Sediq Aziz, Ali Salehi, Mohammadreza Yadegari, Kewyar Yadegari, Sedighe Zamani Roodsari
| Challenge: | UNESCO has identified 60 varieties of Middle Eastern languages as underrepresented . a limited availability of language technology perpetuates a cycle of digital exclusion . |
| Approach: | They develop a parallel corpora for eight severely under-resourced varieties in the region . they evaluate machine translation capabilities through zero-shot approaches and fine-tuning experiments . |
| Outcome: | The proposed model aims to improve the processing of the eight under-resourced languages in the Middle East. |
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model (2021.acl-long)
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| Challenge: | Stereotypical language expresses widely-held beliefs about different social categories. |
| Approach: | They propose a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. |
| Outcome: | The proposed model compares favourably with survey-based studies in the psychological literature on stereotypes and shows that it is realistic and effective. |
Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse (2024.emnlp-main)
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| Challenge: | Using large language models, large language model learning has become more integrated into our daily lives, making it increasingly important to ensure they reflect ethical and equitable values. |
| Approach: | They assess how LLMs can apply moral reasoning to both criticize and defend sexist language by evaluating their models and evaluating the moral foundations cited by them. |
| Outcome: | The models show they can provide comprehensible and contextually relevant text for understanding diverse views on how sexism is perceived. |