The translator's visibility: Detecting translatorial fingerprints in contemporaneous parallel translations. (November 2018) - Lynch, Gerard; Vogel, Carl
Highlights: We examine the stylistic fingerprint of two sets of literary translators. We use machine learning techniques such as SVMs, Nave Bayes and Simple Logistic Regression which are relatively novel approaches to the task. We find that common words and bigrams are strong discriminators of translators style based on two authors, who original wrote in Russian and Norwegian and were translated to English. We find that translators style remains discriminable across non-parallel translations of the same author. We also find that textual metrics such as common verb frequencies are strong indicators of translators style in both cases, in line with prior studies in the literature. Abstract: We detail the results of experiments towards a fine-grained stylometric analysis, the identification of distinguishing features between contemporaneous literary translations, both parallel works and also translations of non-parallel sets of works by the same author. We examine translations of plays by the Norwegian dramatist Henrik Ibsen with the initial point of focus being the Ibsen drama Ghosts, for which there exists comparable contemporaneous translations by R. Farqhuarson Sharp and William Archer. Consequently, a number of prose translations of Russian author Anton Chekhov by Marian Fell and Constance Garnett are examined in order to validate hypotheses formed from the results of the Ibsen study and investigate possible particularities in translator's style which may vary according to genre. By carrying out an analysis of these texts using a variety of machine learning approaches such as Support Vector Machines, Simple Logistic Regression, Naïve Bayes and Decision Tree classifiers, a number of distinguishing textual features are obtained, and the relative frequency of these features in the texts are compared to their frequencies in reference corpora in order to establish which features can be attributed to stylistic choices by the translators themselves and which features may be due to influence from the source language or the topic or genre of a text. We also use the popular Delta metric from authorship attribution studies to investigate the clustering of texts based on most frequent words and a list of discriminatory terms learned in the supervised machine learning experiments. We find that common word unigrams and bigrams are the most salient features for translator fingerprinting across our two authors and four translators examined and are ultimately successful in our goal of classifying which text originated from a particular translator with accuracy measurements of over 90% on average.