We explore the feasibility of applying machine
translation (MT) to the translation of literary
texts. To that end, we measure the translatability
of literary texts by analysing parallel
corpora and measuring the degree of freedom
of the translations and the narrowness of the
domain. We then explore the use of domain
adaptation to translate a novel between two related
languages, Spanish and Catalan. This
is the first time that specific MT systems are
built to translate novels. Our best system outperforms
a strong baseline by 4.61 absolute
points (9.38% relative) in terms of BLEU and
is corroborated by other automatic evaluation
metrics. We provide evidence that MT can
be useful to assist with the translation of novels
between closely-related languages, namely
(i) the translations produced by our best system
are equal to the ones produced by a professional
human translator in almost 20% of
cases with an additional 10% requiring at most
5 character edits, and (ii) a complementary human
evaluation shows that over 60% of the
translations are perceived to be of the sam