Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration

By Ottokar Tilk, Tanel Alumäe. 2016

197 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cordelia Schmid
2. Ivan Laptev
3. Josef Sivic
4. Dima Damen
5. Antoine Miech
6. Andrew Zisserman
7. Walterio W. Mayol-Cuevas
8. Luc Van Gool

In Natural Language Processing

1. Ivan Laptev
2. Walterio W. Mayol-Cuevas
3. Antoine Miech
4. Dima Damen
5. Josef Sivic
6. Antoine Yang
7. Cordelia Schmid
8. Hazel Doughty

In Model Architectures

1. Walterio W. Mayol-Cuevas
2. Ivan Laptev
3. Dima Damen
4. Josef Sivic
5. Antoine Miech
6. Antoine Yang
7. Cordelia Schmid
8. Hazel Doughty

Citations in the corpus

• Just Ask: Learning to Answer Questions from Millions of Narrated Videos (2020, positive): “For punctuation p, we use the BRNN model from [76] and the weights available at [2] trained on IWSLT2011 [23].”
• Action Modifiers: Learning From Adverbs in Instructional Videos (2019, methodology): “First we employ T-BRNN [48] to punctuate the subtitles1, then perform Part-of-Speech (POS) tagging with SpaCy’s English core web model.”