Dynamic adaptation of neural machine-translation systems through translation exemplars

Dynamische aanpassing van neurale automatische vertaalsystemen met behulp van/aan de hand van voorbeelden
Start - End 
2020 - 2023 (ongoing)
Department of Translation, Interpreting and Communication



The advances in the field of neural machine translation (NMT) have

both led to an exciting leap forward in translation quality and

motivated scholars to re-examine the models of the human

translation process. Even though NMT systems are good at

simulating rule-like language behaviour and making generalisations,

they tend to overgeneralise and they tend to forget infrequent

translation patterns while generating translations. Recent work

showed that the quality of NMT systems can substantially be

improved by integrating explicit translation exemplars into the NMT

architecture. These improvements in translation quality brings up

new and exciting research questions about the role of exemplars in

the field of MT, such as the importance of string-based, semantic and

syntactic similarity measures in finding useful exemplars, which can

be transferred to the human translation process and language


This research proposal uses techniques from computational

linguistics to (i) study the impact of adapting NMT systems through

exemplars; (ii) determine the role of different similarity levels, e.g.

string-based, semantic and syntactic, in retrieving useful translation

exemplars; examine (iii) whether the improvements in translation

quality can be observed in different domains, language pairs; and (iv)

whether translation exemplars can successfully be used to adapt

general-domain NMT systems towards specific domains.