  Computer-aided translation (CAT) tools based on translation memories (MT)
play a prominent role in the translation workflow of professional translators.
However, the reduced availability of in-domain TMs, as compared to in-domain
monolingual corpora, limits its adoption for a number of translation tasks. In
this paper, we introduce a novel neural approach aimed at overcoming this
limitation by exploiting not only TMs, but also in-domain target-language (TL)
monolingual corpora, and still enabling a similar functionality to that offered
by conventional TM-based CAT tools. Our approach relies on cross-lingual
sentence embeddings to retrieve translation proposals from TL monolingual
corpora, and on a neural model to estimate their post-editing effort. The paper
presents an automatic evaluation of these techniques on four language pairs
that shows that our approach can successfully exploit monolingual texts in a
TM-based CAT environment, increasing the amount of useful translation
proposals, and that our neural model for estimating the post-editing effort
enables the combination of translation proposals obtained from monolingual
corpora and from TMs in the usual way. A human evaluation performed on a single
language pair confirms the results of the automatic evaluation and seems to
indicate that the translation proposals retrieved with our approach are more
useful than what the automatic evaluation shows.
