The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation

arXiv:2310.20620v2 Announce Type: replace-cross
Abstract: Continuous-output neural machine translation (CoNMT) replaces the discrete next-word prediction problem with an embedding prediction. The semantic structure of the target embedding space (i.e., closeness of related words) is intuitively believed to be crucial. We challenge this assumption and show that completely random output embeddings can outperform laboriously pretrained ones, especially on larger datasets. Further investigation shows this surprising effect is strongest for rare words, due to the geometry of their embeddings. We shed further …

abstract arxiv challenge continuous cs.cl cs.lg embedding embeddings machine machine translation neural machine translation next prediction random semantic show space translation type word words