TY - JOUR
T1 - Markers of translator gender: do they really matter?
AU - Shlesinger, M
AU - Koppel, M
AU - Ordan, N
AU - Malkiel, B
PY - 2009
Y1 - 2009
N2 - Given the impressive record of machine learning in telling male- from
female-authored texts in various genres, we asked whether the computer
could also be “taught” to tell male- from female-translated texts. Our
corpus, downloaded from the website of Words Without Borders, consisted
of 273 samples of literary prose translated into English from a variety of
languages. We found that despite its ability to isolate particular features of
male- vs. female-translated texts, the computer could not be trained to
accurately predict the gender of the translator. We see the difference
between our results and those for original texts as highlighting the
limitations of the classical social-science methodologies; i.e.
notwithstanding the successful application of methods for isolating discrete
features of male-translated vs. female-translated texts, these features were
found to have little or no predictive value when tested in a cross-validation
experiment. In other words, the same cross-validation approach that has
been shown to be highly predictive in the case of author-gender attribution
has proven unreliable for translator-gender attribution. We explore the
implications of these results, both with regard to the competing
methodologies and in terms of their implications for Translation Studies.
AB - Given the impressive record of machine learning in telling male- from
female-authored texts in various genres, we asked whether the computer
could also be “taught” to tell male- from female-translated texts. Our
corpus, downloaded from the website of Words Without Borders, consisted
of 273 samples of literary prose translated into English from a variety of
languages. We found that despite its ability to isolate particular features of
male- vs. female-translated texts, the computer could not be trained to
accurately predict the gender of the translator. We see the difference
between our results and those for original texts as highlighting the
limitations of the classical social-science methodologies; i.e.
notwithstanding the successful application of methods for isolating discrete
features of male-translated vs. female-translated texts, these features were
found to have little or no predictive value when tested in a cross-validation
experiment. In other words, the same cross-validation approach that has
been shown to be highly predictive in the case of author-gender attribution
has proven unreliable for translator-gender attribution. We explore the
implications of these results, both with regard to the competing
methodologies and in terms of their implications for Translation Studies.
UR - https://scholar.google.co.il/scholar?q=Markers+of+translator+gender%3A+do+they+really+matter%3F+&btnG=&hl=en&as_sdt=0%2C5
M3 - Article
SP - 185
EP - 198
JO - Internet. Disponível em http://u. cs. biu. ac. il/~ koppel/Publications. ht ml (consultado em 31de março de 2011)
JF - Internet. Disponível em http://u. cs. biu. ac. il/~ koppel/Publications. ht ml (consultado em 31de março de 2011)
ER -