Digital literacy means being able to understand and use technology. It relates to the ability to find, use and create information online in a beneficial and useful
way.
Machine Translation (MT) tools are simple to use but using them critically requires some thought. It basically comes down to being an informed and critical
user of this technology, rather than being someone who just copies, pastes and clicks.
In this paper, I would like to advocate for a more understanding of MT literacy. For this purpose, I will review current 5 gender bias studies in MT and
summarize them.
Stanovsky et al. (2019) conducted a large-scale multilingual evaluation and the study shows that the adjectives such as “handsome” and “pretty” affect the
translation automatically. Also, while “doctor” biases towards a male translation, “nurse” tugs the translation towards a female inflection.
the term paper should look like:
Introduction
Study 1
Study 2
Study 3
Study 4
Study 5
Conclusion
Biography (Works Cited)
-Evaluating Gender Bias in Machine Translation Stanovsky et al. (2019)
-Gender Bias in Machine Translation Savoldi et al. (2021)
- min.3 more studies
Sample Solution
