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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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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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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Author: Dr. Jingyi Han, Machine Translation Scientist @ Iconic Introduction...
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Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction...
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Author: Dr. Jingyi Han, Machine Translation Scientist @ Iconic</BR Introduction...
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Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction...
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Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction...
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Author: Dr. Jingyi Han, Machine Translation Scientist @ Iconic Introduction...
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