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 behaviour.
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.