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.

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.

This is a well-done paper. It attacks a problem that is worthwhile: how to construct and train a sequence-to-sequence model that can operate on-line instead of waiting for an entire input to be received. It clearly describes an architecture for solving the problem, and walks the reader through the issues in the design of each component in the architecture: next-step prediction, the attention mechanism, and modeling the ends of blocks. It clearly explains the challenges that need to be overcome train the model and perform inference with it, and proposes reasonable approximate algorithms for training and inference. The speech recognition experiments used to demonstrate the utility of the transducer model and to explore design issues such as maintenance of recurrent state across block boundaries, block size, design of the attention mechanism, and depth of the model are reasonable.

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.