Summary of Gost-mt: a Knowledge Graph For Occupation-related Gender Biases in Machine Translation, by Orfeas Menis Mastromichalakis et al.
GOSt-MT: A Knowledge Graph for Occupation-related Gender Biases in Machine Translation
by Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Eva Tsouparopoulou, Dimitris Parsanoglou, Maria Symeonaki, Giorgos Stamou
First submitted to arxiv on: 17 Sep 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 novel approach addresses occupation-related gender bias in machine translation (MT) by introducing the GOSt-MT Knowledge Graph. The graph integrates comprehensive gender statistics from real-world labour data and textual corpora used in MT training, allowing for a detailed analysis of gender bias across English, French, and Greek. This research aims to identify persistent stereotypes and areas requiring intervention, ultimately contributing to making MT systems more equitable and reducing gender biases in automated translations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make machine translation better by removing unfair gender stereotypes. It creates a special tool called GOSt-MT that looks at how people’s jobs are related to their genders. The tool shows where these biases are present in three languages: English, French, and Greek. By understanding these biases, researchers can work on making translations fairer and reducing gender-based mistakes. |
Keywords
» Artificial intelligence » Knowledge graph » Translation
