Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation
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).
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTTranslationSimilar Papers 제목 키워드 기반
Lexicogrammatic translationese across two targets and competence levels
This research employs genre-comparable data from a number of parallel and comparable corpora to explore the specificity of translations from English into German and Russian produced by students and professional translato…
SpecificityTranslationVocal Bursts Valence PredictionTranslating away Translationese without Parallel Data
Translated texts exhibit systematic linguistic differences compared to original texts in the same language, and these differences are referred to as translationese. Translationese has effects on various cross-lingual nat…
Binary ClassificationLanguage ModellingSemantic SimilaritySemantic Textual Similarity+1Translationese in Russian Literary Texts
The paper reports the results of a translationese study of literary texts based on translated and non-translated Russian. We aim to find out if translations deviate from non-translated literary texts, and if the establis…
SpecificityTowards Debiasing Translation Artifacts
Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets. However, compared to original texts in the same…
Natural Language InferenceSentenceTranslationMitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneck
Large language models (LLMs) have become a standard for multilingual evaluation, yet they exhibit a severe systematic translationese bias. In this paper, translationese bias is characterized as LLMs systematically favori…