  Building Machine Translation (MT) systems for low-resource languages remains
challenging. For many language pairs, parallel data are not widely available,
and in such cases MT models do not achieve results comparable to those seen
with high-resource languages.
  When data are scarce, it is of paramount importance to make optimal use of
the limited material available. To that end, in this paper we propose employing
the same parallel sentences multiple times, only changing the way the words are
split each time. For this purpose we use several Byte Pair Encoding models,
with various merge operations used in their configuration.
  In our experiments, we use this technique to expand the available data and
improve an MT system involving a low-resource language pair, namely
English-Esperanto.
  As an additional contribution, we made available a set of English-Esperanto
parallel data in the literary domain.
