# Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1703.04650 · HTML: https://mlsanity.com/paper/1703.04650/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2017.

## Built on by (most cited first, 6 of 6)

- [Capitalization and Punctuation Restoration: a Survey](https://mlsanity.com/paper/2111.10746/) (2021)
- [Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios](https://mlsanity.com/paper/2109.06103/) (2021)
- [Incorporating External POS Tagger for Punctuation Restoration](https://mlsanity.com/paper/2106.06731/) (2021)
- [Vietnamese Capitalization and Punctuation Recovery Models](https://mlsanity.com/paper/2207.01312/) (2022)
- [Text Injection for Capitalization and Turn-Taking Prediction in Speech Models](https://mlsanity.com/paper/2308.07395/) (2023)
- [A light-weight and efficient punctuation and word casing prediction model for on-device streaming ASR](https://mlsanity.com/paper/2407.13142/) (2024)

## Abstract (arXiv)

The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting. Most natural language processing applications expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modeling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method could be extended for joint modeling of any other correlated sequence labeling tasks.