Computer Science > Machine Learning
[Submitted on 30 Oct 2018 (v1), last revised 5 Sep 2019 (this version, v2)]
Title:Long Short-Term Attention
View PDFAbstract:Attention is an important cognition process of humans, which helps humans concentrate on critical information during their perception and learning. However, although many machine learning models can remember information of data, they have no the attention mechanism. For example, the long short-term memory (LSTM) network is able to remember sequential information, but it cannot pay special attention to part of the sequences. In this paper, we present a novel model called long short-term attention (LSTA), which seamlessly integrates the attention mechanism into the inner cell of LSTM. More than processing long short term dependencies, LSTA can focus on important information of the sequences with the attention mechanism. Extensive experiments demonstrate that LSTA outperforms LSTM and related models on the sequence learning tasks.
Submission history
From: Guoqiang Zhong [view email][v1] Tue, 30 Oct 2018 14:08:30 UTC (162 KB)
[v2] Thu, 5 Sep 2019 02:44:15 UTC (196 KB)
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