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. |
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| Challenge: | a new corpus-level evaluation approach for event extraction is needed in social science applications . human annotations are often required to extract the actions of political actors and actors . a novel corpus evaluation approach can guide creation of similar social science-oriented resources . |
| Approach: | They propose a corpus-based approach to event extraction that integrates corpus evaluation with real-world social science . they use human annotations to read and label every document for mentions of police activity events . |
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HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter (2025.acl-long)
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Manuel Tonneau, Diyi Liu, Niyati Malhotra, Scott A. Hale, Samuel Fraiberger, Victor Orozco-Olvera, Paul Röttger
| Challenge: | Prior work on automated hate speech detection models has been limited due to systematic biases in evaluation datasets and poor performance across geographies. |
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Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection (2024.emnlp-main)
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| Challenge: | Recent work on synthetic data for training models for NLP tasks reports mixed results on subjective tasks such as hate speech detection. |
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Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021.acl-long)
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| Challenge: | Detecting online hate speech has proven difficult and concerns raised about performance, robustness, generalisability and fairness of stateof-the-art models. |
| Approach: | They propose a human-and-model-in-the-loop process for dynamically generating datasets and training better performing hate detection models. |
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What Did You Learn To Hate? A Topic-Oriented Analysis of Generalization in Hate Speech Detection (2023.eacl-main)
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| Challenge: | Hate speech detection datasets often use different annotation guidelines, resulting in inconsistencies . authors propose a topic-oriented approach to study generalization across popular hate speech datasets . |
| Approach: | They propose a topic-oriented approach to study generalization across popular hate speech datasets . they compare Transformer-based models in capturing topic-generic and topic-specific knowledge . |
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Improving Event Definition Following For Zero-Shot Event Detection (2024.acl-long)
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Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Ma, Hritik Bansal, Baobao Chang, P. Jeffrey Brantingham, Wei Wang, Nanyun Peng
| Challenge: | Existing approaches on zero-shot event detection train models on datasets annotated with known event types and prompt them with unseen event definitions. |
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Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
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Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
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Adversarial Training for Weakly Supervised Event Detection (N19-1)
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| Challenge: | Detecting and identifying events is an important subtask of event extraction. |
| Approach: | They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones. |
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APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets (2022.findings-emnlp)
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| Challenge: | flaming or trolling in online communities is considered hostile behavior . a dataset of hate speech examples can be useful for detecting toxic or pejorative expressions . annotating on existing web text has several limitations that deter the dataset's reliability . |
| Approach: | They propose a dataset that asks users to generate hate speech examples followed by minimal post-labeling. |
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