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
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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 |