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

TL;DR

This work addresses the scarcity of in-domain translation memories by enabling CAT to exploit in-domain target-language monolingual corpora. It introduces neuroMatch, which retrieves translation proposals from TL corpora using cross-lingual sentence embeddings, and neuroFMS, a COMET-based predictor that estimates post-editing effort for those proposals. Across four language pairs, neuroMatch increases useful proposals relative to conventional FMS, and neuroFMS enables effective combination of TM- and monolingual-derived proposals, with human evaluation corroborating the automatic findings. The approach promises practical gains in translator productivity by expanding the pool of usable proposals and providing a unified post-editing effort signal across heterogeneous sources.

Abstract

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.

Figures (1)

• Figure 1: Percentage of sentences in the test set for which a translation proposal is obtained with FMS on TM and with neuroMatch on TM+mono when TER is below 0.4, 0.3, 0.2 and 0.1.