On the features of translationese

By Vered Volansky, Noam Ordan, Shuly Wintner. 2015

217 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Language and Linguistics, Natural Language Processing

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 Language and Linguistics

In Natural Language Processing

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 andToutanova, 2014; Rabinovich and Wintner, 2015; Wintner, 2016).”
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning (2020, background): “…it is prone to compromise the latter goal, bending the target language to the structural and lexical properties of the source language: the resulting evaluation benchmarks thus fail to measure system performance adequately (Koppel and Ordan, 2011; Volansky et al., 2015; Artetxe et al., 2020a).”
Personalized Machine Translation: Preserving Original Author Traits (2016, background): “Recent corpus-based investigations of translationese demonstrated that originals and translations are distinguishable by means of supervised and un-supervised classiﬁcation (Baroni and Bernardini, 2006; Volansky et al., 2015; Rabinovich and Wintner, 2015).”
Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation (2016, background): “3/23/16, 9:02 AM Although Volansky et al. (2013) show that generalization happens in translation too, it is likely more frequent in Interpretese given the severe time constraints.”
Unsupervised Identification of Translationese (2015, methodology): “We ﬁrst reproduce the Europarl classiﬁcation results with the best performing feature sets, as reported by Volansky et al. (2015), and present results for three additional sub-corpora: Hansard, Literature and TED.”
Data Diversification: A Simple Strategy For Neural Machine Translation (2019, background): “However, in a recent work, Edunov et al. [5] point out that back-translation suffers from the translationese effect [25], where back-translation only improves the performance when the source sentences are translationese but does not offer any improvement when the sentences are natural text.”
Asymmetric Features Of Human Generated Translation (2014, background): “Patterns of Translationese can be categorized as follows (Volansky et al., 2013): 1.”
A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages (2017, background): “…in the translations from the English and French sources, we noticed the most variations fall into two categories: Source Language Bias Since different languages have different styles of writing, these differences are reﬂected in translations from different language sources (Volansky et al., 2015).”
Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus (2026, unknown): “These traits include awkward or unidiomatic phrasing due to overly literal renderings of the source, as well as systematic differences in lexical choice and syntax [67].”