CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Self-Attention Based Network for Punctuation Restoration
Feng Wang; Wei Chen; Zhen Yang; Bo Xu
2018
Conference NameInternational Conference on Pattern Recognition
Conference DateAugust 20th-24th 2018
Conference PlaceIn Beijing, China
Abstract
Inserting proper punctuation into Automatic Speech
Recognizer(ASR) transcription is a challenging and promising
task in real-time Spoken Language Translation(SLT). Traditional
methods built on the sequence labelling framework are weak
in handling the joint punctuation. To tackle this problem, we
propose a novel self-attention based network, which can solve the
aforementioned problem very well. In this work, a light-weight
neural net is proposed to extract the hidden features based solely
on self-attention without any Recurrent Neural Nets(RNN) and
Convolutional Neural Nets(CNN). We conduct extensive experiments
on complex punctuation tasks. The experimental results
show that the proposed model achieves significant improvements
on joint punctuation task while being superior to traditional
methods on simple punctuation task as well.
KeywordPunctuation Restoration Self-attention
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22101
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorWei Chen
AffiliationInstitute of Automation Chinese Academy of Science
Recommended Citation
GB/T 7714
Feng Wang,Wei Chen,Zhen Yang,et al. Self-Attention Based Network for Punctuation Restoration[C],2018.
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