Multi-scale Alignment and Contextual History for Attention Mechanism in Sequence-to-sequence Model
Abstract
A sequence-to-sequence model is a neural network module for mapping two sequences of different lengths. The sequence-to-sequence model has three core modules: encoder, decoder, and attention. Attention is the bridge that connects the encoder and decoder modules and improves model performance in many tasks. In this paper, we propose two ideas to improve sequence-to-sequence model performance by enhancing the attention module. First, we maintain the history of the location and the expected context from several previous time-steps. Second, we apply multiscale convolution from several previous attention vectors to the current decoder state. We utilized our proposed framework for sequence-to-sequence speech recognition and text-to-speech systems. The results reveal that our proposed extension could improve performance significantly compared to a standard attention baseline.
Cite
@article{arxiv.1807.08280,
title = {Multi-scale Alignment and Contextual History for Attention Mechanism in Sequence-to-sequence Model},
author = {Andros Tjandra and Sakriani Sakti and Satoshi Nakamura},
journal= {arXiv preprint arXiv:1807.08280},
year = {2018}
}