Summary of The First Multilingual Model For the Detection Of Suicide Texts, by Rodolfo Zevallos et al.
The First Multilingual Model For The Detection of Suicide Texts
by Rodolfo Zevallos, Annika Schoene, John E. Ortega
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a multilingual model using transformer architectures like mBERT, XML-R, and mT5 to detect suicidal text across posts in six languages: Spanish, English, German, Catalan, Portuguese, and Italian. The model is fine-tuned on a multilingual dataset translated from Spanish to the other five languages using SeamlessM4T. Evaluation metrics show that mT5 achieves the best performance overall with F1 scores above 85%, demonstrating cross-lingual transfer learning capabilities. The English and Spanish translations also display high quality based on perplexity. This work highlights the importance of considering linguistic diversity in developing automated multilingual tools to identify suicidal risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help people who are thinking about harming themselves by looking at social media posts. The researchers used special computer models that can understand many languages, like Spanish, English, and Italian. They tested these models on a big dataset of social media posts and found that one model, called mT5, was the best at detecting when someone might be thinking about suicide. This is important because it helps us develop tools that can reach people who speak different languages and understand their struggles. |
Keywords
» Artificial intelligence » Perplexity » Transfer learning » Transformer
