The Neural MT Weekly

NMT 135 Recovering Low-Frequency Words in Non-Autoregressive Neural MT

Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction Non-Autoregressive Translation (NAT), in which the target words are generated independently, is raising a lot of interest because of its efficiency. However, the assumption that target words are independent of each other leads to errors which affect translation quality. In this post we take a look at a paper by Ding et al. (2021) which confirms...

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NMT 134 A Targeted Attack on Black-Box Neural Machine Translation

Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction Last week we looked at how neural machine translation (NMT) systems are naturally susceptible to gender bias. In today’s blog post we look at the vulnerability of an NMT system to targeted attacks, which could result in unsolicited or harmful translations. Specifically we report on work by Xu et al., 2021, which examines attacks on black-box NMT...

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NMT 133 Evaluating Gender Bias in Machine Translation

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...

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