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.

This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by augmenting an additional alignment head to the multi-head source-to-target attention component. This is used to compute sharper attention weights. We describe how to use the alignment head to achieve competitive performance. To study the effect of adding the alignment head, we simulate a dictionaryguided translation task, where the user wants to guide translation using pre-defined dictionary entries. Using the proposed approach, we achieve up to 3.8% BLEU improvement when using the dictionary, in comparison to 2.4% BLEU in the baseline case. We also propose alignment pruning to speed up decoding in alignment-based neural machine translation (ANMT), which speeds up translation by a factor of 1.8 without loss in translation performance. We carry out experiments on the shared WMT 2016 English→Romanian news task and the BOLT Chinese→English discussion forum task. 1 The transformer models won in most of the WMT 2018 news translation tasks:

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