Papers by Mohammad Atari
Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese (2024.emnlp-main)
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| Challenge: | Humans have produced written language for thousands of years, but most computational work is focused on contemporary languages and corpora. |
| Approach: | They propose a pipeline for historical-psychological text analysis in classical Chinese . they propose an indirect contrastive learning approach that fine-tunes pre-trained models . |
| Outcome: | The proposed pipeline outperforms word-embedding-based approaches across all tasks and exceeds prompting with GPT-4 in most tasks. |
Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model (2023.acl-long)
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Ali Omrani, Alireza Salkhordeh Ziabari, Charles Yu, Preni Golazizian, Brendan Kennedy, Mohammad Atari, Heng Ji, Morteza Dehghani
| Challenge: | Existing methods for mitigating bias require social-group-specific word pairs for each social attribute (e.g., gender) Existing approaches require only one social attribute, rendering them impractical and costly . |
| Approach: | They propose that stereotype content models capture the underlying connection between bias and stereotypes by embedding only two psychological dimensions of warmth and competence. |
| Outcome: | The proposed method performs comparably to group-specific debiasing on multiple bias benchmarks, but has theoretical and practical advantages over existing methods. |
Hate Speech Classifiers Learn Normative Social Stereotypes (2023.tacl-1)
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| Challenge: | Social stereotypes negatively impact individuals’ judgments about different groups and may have a critical role in understanding language directed toward marginalized groups. |
| Approach: | They first investigate the impact of novice annotators’ stereotypes on their hate-speech-annotation behavior. Then, they examine the effect of normative stereotypes in language on the aggregated annotated judgments. |
| Outcome: | The framework provides insights into sources of bias in hate-speech moderation, informing ongoing debates regarding machine learning fairness. |
Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes (D19-1)
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Aida Mostafazadeh Davani, Leigh Yeh, Mohammad Atari, Brendan Kennedy, Gwenyth Portillo Wightman, Elaine Gonzalez, Natalie Delong, Rhea Bhatia, Arineh Mirinjian, Xiang Ren, Morteza Dehghani
| Challenge: | Existing estimates of hate crimes in the US are under-reported relative to actual number of incidents. |
| Approach: | They propose to use event extraction and multi-instance learning to predict hate crimes in local news articles for cities without official FBI reports. |
| Outcome: | The proposed model compares to FBI reports and shows that hate crimes are under-reported in local press. |