Unsupervised Identification of Translationese

By Ella Rabinovich, Shuly Wintner. 2015

55 citations recorded by the source corpus.

Translated texts are distinctively different from original ones, to the extent that supervised text classification methods can distinguish between them with high accuracy. These differences were proven useful for statistical machine translation. However, it has been suggested that the accuracy of translation detection deteriorates when the classifier is evaluated outside the domain it was trained on. We show that this is indeed the case, in a variety of evaluation scenarios. We then show that unsupervised classification is highly accurate on this task. We suggest a method for determining the correct labels of the clustering outcomes, and then use the labels for voting, improving the accuracy even further. Moreover, we suggest a simple method for clustering in the challenging case of mixed-domain datasets, in spite of the dominance of domain-related features over translation-related ones. The result is an effective, fully-unsupervised method for distinguishing between original and translated texts that can be applied to new domains with reasonable accuracy.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

In Machine Translation

Citations in the corpus

TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020, background): “The result is that a corpus of so-called Translationese may be markedly different from purely native text (Lembersky et al., 2012; Volansky et al., 2013; Avner et al., 2014; Eetemadi and Toutanova, 2014; Rabinovich and Wintner, 2015; Wintner, 2016).”
Personalized Machine Translation: Preserving Original Author Traits (2016, background): “While modeling of demographic traits has been proven beneficial in some NLP tasks such as sentiment analysis (Volkova et al., 2013) or topic classification (Hovy, 2015), very little attention has been paid to translation.”
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2020, background): “Recent investigations into cross-lingual modeling have revealed “translation artifacts" in datasets where machine translation systems are used, or human translation tasks are not carefully curated (Artetxe et al., 2020a; Wintner, 2016; Rabinovich and Wintner, 2015).”
High-Dimension Human Value Representation in Large Language Models (2024, unknown): “We then prompt the 12 LLMs used shown in Table 1 to generate responses to the questions.”