08/12/2020

Bayes-enhanced Lifelong Attention Networks for Sentiment Classification

Hao Wang, Shuai Wang, Sahisnu Mazumder, Bing Liu, Yan Yang, Tianrui Li

Keywords:

Abstract Paper Similar Papers

Abstract: The classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task. This paper studies the problem of learning a sequence of tasks (sentiment classification tasks in our case). After each sentiment classification task is learned, its knowledge is retained to help future task learning. Following this setting, we explore attention neural networks and propose a Bayes-enhanced Lifelong Attention Network (BLAN). The key idea is to exploit the generative parameters of naive Bayes to learn attention knowledge. The learned knowledge from each task is stored in a knowledge base and later used to build lifelong attentions. The constructed lifelong attentions are then used to enhance the attention of the network to help new task learning. Experimental results on product reviews from Amazon.com show the effectiveness of the proposed model.

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6289-bayes-enhanced-lifelong-attention-networks-for-sentiment-classification
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at COLING 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 14:23 
 0
 0
 0
 0
 14:58 
 0
 0
 0
 0
 5:09 
 0
 0
 0
 0
 2:57 
 0
 0
 0
 1
 3:21 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 2:54 
 0
 0
 0
 0
 15:13 
 0
 0
 0
 0
 12:53 
 0
 0
 0
 0
 10:59 
 0
 0
 0
 0
 3:00 
 0
 0
 0
 0
 7:39 
 0
 0
 0
 0
 17:05 
 0
 0
 0
 0
 13:45 
 0
 0
 0
 0
 12:22 
 0
 0
 0
 0
 5:23 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 11:46 
 0
 0
 0
 0
 7:09 
 0
 0
 0
 0
 2:45 
 0
 0
 0
 0
 15:57 
 0
 1
 1
 1
 3:24 
 0
 0
 0
 0
 3:06 
 0
 0
 0
 0
 17:42 
 0
 0
 0
 0
 3:09 
 0
 0
 0
 0
 11:11 
 0
 0
 0
 0
 6:46 
 0
 0
 0
 0
 13:57 
 0
 0
 0
 0
 15:34 
 0
 0
 0
 0
 10:10 
 0
 0
 0
 0
 15:28 
 0
 0
 0
 0
 8:27 
 0
 0
 0
 0
 13:41 
 0
 0
 0
 0
 7:13 
 0
 0
 0
 0
 11:25 
 0
 0
 0
 0
 5:07 
 0
 0
 0
 0
 5:46 
 0
 0
 0
 0
 5:36 
 0
 0
 0
 0
 4:31 
 0
 0
 0
 0
 9:58