Priming Neural Machine Translation
Minh Quang Pham, Jitao Xu, Josep Crego, François Yvon, Jean Sénellart · 2020
Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response.In this paper, we propose a framework to mimic the process of priming in the context of neural machine translation (NMT).We evaluate the effect of using similar translations as priming cues on the NMT network.We propose a method to inject priming cues into the NMT network and compare our framework to other mechanisms that perform micro-adaptation during inference.Overall, experiments conducted in a multi-domain setting confirm that adding priming cues in the NMT decoder can go a long way towards improving the translation accuracy.Besides, we show the suitability of our framework to gather valuable information for an NMT network from monolingual resources.