Summary of Generating Gender Alternatives in Machine Translation, by Sarthak Garg et al.
Generating Gender Alternatives in Machine Translation
by Sarthak Garg, Mozhdeh Gheini, Clara Emmanuel, Tatiana Likhomanenko, Qin Gao, Matthias Paulik
First submitted to arxiv on: 29 Jul 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 A machine learning-based approach to resolving gender ambiguity in machine translation (MT) systems is proposed, aiming to generate all grammatically correct gendered translation alternatives. The current practice of perpetuating harmful stereotypes by translating terms with ambiguous gender into the most prevalent form in the training data is addressed. To achieve this, a novel semi-supervised solution is introduced, which integrates seamlessly with standard MT models and maintains high performance without increasing inference overhead or requiring additional components. This study opens source train and test datasets for five language pairs, establishing benchmarks for this task. The proposed approach has implications for improving MT user interfaces that allow for resolving gender ambiguity in a frictionless manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine translation systems often translate terms with ambiguous gender into the most common form found in their training data. This can perpetuate harmful stereotypes and biases. Researchers have developed a way to generate all possible translations of terms with ambiguous gender, which could help reduce these biases. They created datasets for five language pairs and established rules for measuring how well different methods perform on this task. The team’s approach works by combining existing machine translation models with new algorithms that don’t require extra processing power or components. |
Keywords
» Artificial intelligence » Inference » Machine learning » Semi supervised » Translation
