Recent studies in the field of Machine Translation (MT) and Natural Language
Processing (NLP) have shown that existing models amplify biases observed in the
training data. The amplification of biases in language technology has mainly
been examined with respect to specific phenomena, such as gender bias. In this
work, we go beyond the study of gender in MT and investigate how bias
amplification might affect language in a broader sense. We hypothesize that the
'algorithmic bias', i.e. an exacerbation of frequently observed patterns in
combination with a loss of less frequent ones, not only exacerbates societal
biases present in current datasets but could also lead to an artificially
impoverished language: 'machine translationese'. We assess the linguistic
richness (on a lexical and morphological level) of translations created by
different data-driven MT paradigms - phrase-based statistical (PB-SMT) and
neural MT (NMT). Our experiments show that there is a loss of lexical and
morphological richness in the translations produced by all investigated MT
paradigms for two language pairs (EN<=>FR and EN<=>ES).

本文研究机器翻译中偏见放大的问题，探究算法的偏见增强对语言是否会产生贫化作用。作者测试了不同的数据驱动机器翻译范式，并发现所有测试的语言对中，都存在词汇和形态上的丧失。