Alignment-Based Neural Machine Translation
Tamer Alkhouli, Gabriel Bretschner, Jan-Thorsten Peter, Mohammed Hethnawi, Andreas Guta, Hermann Ney · 2016
Neural machine translation (NMT) has emerged recently as a promising statistical machine translation approach.In NMT, neural networks (NN) are directly used to produce translations, without relying on a pre-existing translation framework.In this work, we take a step towards bridging the gap between conventional word alignment models and NMT.We follow the hidden Markov model (HMM) approach that separates the alignment and lexical models.We propose a neural alignment model and combine it with a lexical neural model in a loglinear framework.The models are used in a standalone word-based decoder that explicitly hypothesizes alignments during search.We demonstrate that our system outperforms attention-based NMT on two tasks: IWSLT 2013 German→English and BOLT Chinese→English.We also show promising results for re-aligning the training data using neural models.