Contrary to perceived wisdom, we explore the role of machine
translation (MT) in assisting with the translation of literary texts,
considering both its limitations and its potential. Our motivations to
explore this subject are twofold: (i) the recent research advances in MT,
and (ii) the recent emergence of the ebook, which together allow us for
the first time to build literature-specific MT systems by training
statistical MT models on novels and their professional translations. A
key challenge in literary translation is that one needs to preserve not
only the meaning (as in other domains such as technical translation) but
also the reading experience, so a literary translator needs to carefully
select from the possible translation options. We explore the role of
translation options in literary translation, especially in the context of the
relatedness of the languages involved. We take Camus‘ L’Étranger in
the original French language and provide qualitative and quantitative
analyses for its translations into English (a less-related language) and
Italian (more closely related). Unsurprisingly, the MT output for Italian
seems more straightforward to be post-edited. We also show that the
performance of MT has improved over the last two years for this book,
and that the applicability of MT does not only depend on the text to be
translated but also on the type of translation that we are trying to
produce. We then translate a novel from Spanish-to-Catalan with a
literature-specific MT system. We assess the potential of this approach
by discussing the translation quality of several representative passages.