Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation

This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of...

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Bibliographic Details
Published in:arXiv.org 2019-06
Main Authors: Vanmassenhove, Eva, Shterionov, Dimitar, Way, Andy
Format: Article
Language:English
Subjects:
Algorithms
Machine translation
Online Access:Get full text
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Summary:This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of MT systems to generate diverse outputs and its tendency to exacerbate already frequent patterns while ignoring less frequent ones, might be the underlying cause for, among others, the currently heavily debated issues related to gender biased output. Can we indeed, aside from biased data, talk about an algorithm that exacerbates seen biases?
ISSN:2331-8422