  Sequence-to-sequence models have achieved impressive results on various
tasks. However, they are unsuitable for tasks that require incremental
predictions to be made as more data arrives or tasks that have long input
sequences and output sequences. This is because they generate an output
sequence conditioned on an entire input sequence. In this paper, we present a
Neural Transducer that can make incremental predictions as more input arrives,
without redoing the entire computation. Unlike sequence-to-sequence models, the
Neural Transducer computes the next-step distribution conditioned on the
partially observed input sequence and the partially generated sequence. At each
time step, the transducer can decide to emit zero to many output symbols. The
data can be processed using an encoder and presented as input to the
transducer. The discrete decision to emit a symbol at every time step makes it
difficult to learn with conventional backpropagation. It is however possible to
train the transducer by using a dynamic programming algorithm to generate
target discrete decisions. Our experiments show that the Neural Transducer
works well in settings where it is required to produce output predictions as
data come in. We also find that the Neural Transducer performs well for long
sequences even when attention mechanisms are not used.
