  Recently, encoder-decoder neural networks have shown impressive performance
on many sequence-related tasks. The architecture commonly uses an attentional
mechanism which allows the model to learn alignments between the source and the
target sequence. Most attentional mechanisms used today is based on a global
attention property which requires a computation of a weighted summarization of
the whole input sequence generated by encoder states. However, it is
computationally expensive and often produces misalignment on the longer input
sequence. Furthermore, it does not fit with monotonous or left-to-right nature
in several tasks, such as automatic speech recognition (ASR),
grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention
mechanism that has local and monotonic properties. Various ways to control
those properties are also explored. Experimental results on ASR, G2P and
machine translation between two languages with similar sentence structures,
demonstrate that the proposed encoder-decoder model with local monotonic
attention could achieve significant performance improvements and reduce the
computational complexity in comparison with the one that used the standard
global attention architecture.
