  Auto-regressive sequence-to-sequence models with attention mechanisms have
achieved state-of-the-art performance in various tasks including Text-To-Speech
(TTS) and Neural Machine Translation (NMT). The standard training approach,
teacher forcing, guides a model with the reference output history. At inference
stage, the generated output history must be used. This mismatch can impact
performance. However, it is highly challenging to train the model using the
generated output. Several approaches have been proposed to address this
problem, normally by selectively using the generated output history. To make
training stable, these approaches often require a heuristic schedule or an
auxiliary classifier. This paper introduces attention forcing for NMT. This
approach guides the model with the generated output history and reference
attention, and can reduce the training-inference mismatch without a schedule or
a classifier. Attention forcing has been successful in TTS, but its application
to NMT is more challenging, due to the discrete and multi-modal nature of the
output space. To tackle this problem, this paper adds a selection scheme to
vanilla attention forcing, which automatically selects a suitable training
approach for each pair of training data. Experiments show that attention
forcing can improve the overall translation quality and the diversity of the
translations.
