Papers by Scott Hale
Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate (2022.naacl-main)
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| Challenge: | Existing models for detecting hate expressed with emojis have weaknesses when used for sensitive applications such as content moderation. |
| Approach: | They propose a test suite of 3,930 short-form statements that evaluates hateful language expressed with emoji. |
| Outcome: | The proposed model performs better on emoji-based hate while maintaining strong performance on text-only hate. |
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings (D19-1)
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| Challenge: | Word embeddings are increasingly used for automatic detection of semantic change, but a robust evaluation and systematic comparison of the choices involved has been lacking. |
| Approach: | They propose a new evaluation framework for semantic change detection using whole time series and a Twitter dataset spanning 5.5 years. |
| Outcome: | The proposed framework shows that using whole time series is preferable over continuously trained embeddings for long time periods and that the reference point matters. |
The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values (2023.emnlp-main)
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| Challenge: | Incorporating human feedback into Large Language Models is a welcome development, but it introduces new biases and challenges. |
| Approach: | They propose to survey 95 articles that use human feedback to steer, guide or tailor the behaviours of large language models. |
| Outcome: | The proposed approaches are based on 95 articles primarily from the ACL and arXiv repositories and highlight five unresolved conceptual and practical challenges. |
Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate (2021.findings-acl)
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| Challenge: | Imlicit hate content has unusual syntax, polysemic words, and fewer markers of prejudice, e.g., slurs . multimodal content is harder to detect than unimodal content, such as memes . |
| Approach: | They evaluate the role of semantic and multimodal context for detecting implicit and explicit hate . they find that all models perform better on content with full annotator agreement . |
| Outcome: | The proposed model outperforms other models on implicit and explicit hate detection tasks because of its lower propensity towards false positives. |
Claim Matching Beyond English to Scale Global Fact-Checking (2021.acl-long)
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| Challenge: | Existing methods to fact-check content are not scaled well in non-English contexts. |
| Approach: | They propose to use a WhatsApp tipline and public group message dataset to find pairs of textual messages containing claims that can be served with one fact-check. |
| Outcome: | The proposed model outperforms existing models in English, Hindi, and Tamil in all settings. |