IWSLT15

AbstractIn this paper we investigate the potential of the neural machine translation (NMT) when taking into consideration the linguistic aspect of target language. From this standpoint, the NMT approach with …...

AbstractIn Neural Machine Translation (NMT) the usage of sub-words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, …...

AbstractNeural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios. Neural models have to be trained on large amounts of data and have been …...

AbstractWe evaluate three simple, normalization-centric changes to improve Transformer training. First, we show that pre-norm residual connections (PRENORM) and smaller initializations enable warmup-free, validation-based training with large learning rates. Second, …...

AbstractLow-resource language translation is a challenging but socially valuable NLP task. Building on recent work adapting the Transformer’s normalization to this setting, we propose QKNorm, a normalization technique that modifies …...

AbstractWe propose a data augmentation method for neural machine translation. It works by interpreting language models and phrasal alignment causally. Specifically, it creates augmented parallel translation corpora by generating (path-specific) …...