Attention-based Encoder-Decoder

The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation. In this paper, we ask a fundamental question: can neural machine …...

AbstractWhile neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. …...

AbstractThis paper describes the Johns Hopkins University (JHU) and Kyoto University submissions to the Speech Translation evaluation campaign at IWSLT2018. Our end-to-end speech translation systems are based on ESPnet and …...

Neural machine translation (NMT) sys- tems operate primarily on words (or sub- words), ignoring lower-level patterns of morphology. We present a character- aware decoder designed to capture such patterns when …...

AbstractText style transfer rephrases a text from a source style (e.g., informal) to a target style (e.g., formal) while keeping its original meaning. Despite the success existing works have achieved …...

The recent emergence of end-to-end automatic speech recognition (ASR) frameworks has streamlined the complicated modeling procedures of ASR systems in contrast to the conventional deep neural network-hidden Markov (DNN-HMM) ASR …...

The attention-based encoder-decoder frame- work is widely used in many natural lan- guage generation tasks. The attention mecha- nism builds alignments between target words and source items that facilitate text …...