2016 BidirectionalRecurrentNeuralNet

• (Tilk & Alumae, 2016) ⇒ Ottokar Tilk, and Tanel Alumae. (2016). “Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration.” In: Proceedings of Interspeech 2016. doi:10.21437/Interspeech.2016

Subject Headings: Attention Mechanism; Memory Augmented Neural Network System

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Bidirectional+Recurrent+Neural+Network+with+Attention+Mechanism+for+Punctuation+Restoration

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Author Keywords

• Neural Network; Punctuation Restoration

Abstract

Automatic speech recognition systems generally produce unpunctuated text which is difficult to read for humans and degrades the performance of many downstream machine processing tasks. This paper introduces a bidirectional recurrent neural network model with attention mechanism for punctuation restoration in unsegmented text. The model can utilize long contexts in both directions and direct attention where necessary enabling it to outperform previous state-of-the-art on English (IWSLT2011) and Estonian datasets by a large margin.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 BidirectionalRecurrentNeuralNet	Ottokar Tilk Tanel Alumae			Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration				10.21437/Interspeech.2016		2016