Summary of A Roadmap For Multilingual, Multimodal Domain Independent Deception Detection, by Dainis Boumber et al.
A Roadmap for Multilingual, Multimodal Domain Independent Deception Detection
by Dainis Boumber, Rakesh M. Verma, Fatima Zahra Qachfar
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 A novel study investigates the universal cues of deception across languages, exploring whether these cues can be applied to detect deceit in multiple linguistic contexts. Researchers examine the current state of deception detection techniques and identify gaps in understanding this phenomenon in low-resource languages. The paper highlights the importance of multimodality in detecting deception, considering various forms of media like images with altered captions. To address the complexity of deceptive language, the authors propose a comprehensive investigation using multilingual transformer models and labeled data from diverse languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deception is when someone says something that isn’t true. In today’s digital world, people are communicating in many different languages online. Researchers want to know if there are certain signs or “cues” that can help detect deception across all these languages. They also look at how technology can be used to spot lies on social media and other online platforms. |
Keywords
» Artificial intelligence » Transformer
