Issue #133 – Evaluating Gender Bias in MT

02 Jun21

Issue #133 – Evaluating Gender Bias in MT

Author: Akshai Ramesh, Machine Translation Scientist @ Iconic

Introduction

We often tend to personify aspects of life that may vary based upon the beholder’s interpretation. There are plenty of examples for this – “Mother Earth”, Doctor (Men), Cricketer (Men), Nurse(Woman), Cook(Woman), etc. The MT systems are trained with a large amount of parallel corpus which encodes this social bias. If that is the case, then to what extent is this misconception passed on to the MT systems? Join us as we dive into one of the interesting research areas of MT: Gender Bias.

In issue #23 of the blog series, we went into the

 

 

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