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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-domainmonolingual corpora, limits its adoption for a number of translation tasks. Inthis paper, we introduce a novel neural approach aimed at overcoming thislimitation by exploiting not only TMs, but also in-domain target-language (TL)monolingual corpora, and still enabling a similar functionality to that offeredby conventional TM-based CAT tools. Our approach relies on cross-lingualsentence embeddings to retrieve translation proposals from TL monolingualcorpora, and on a neural model to estimate their post-editing effort. The paperpresents an automatic evaluation of these techniques on four language pairsthat shows that our approach can successfully exploit monolingual texts in aTM-based CAT environment, increasing the amount of useful translationproposals, and that our neural model for estimating the post-editing effortenables the combination of translation proposals obtained from monolingualcorpora and from TMs in the usual way. A human evaluation performed on a singlelanguage pair confirms the results of the automatic evaluation and seems toindicate that the translation proposals retrieved with our approach are moreuseful than what the automatic evaluation shows.

Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation