# Improving Recurrent Neural Networks For Sequence Labelling

arXiv: https://arxiv.org/abs/1606.02555 · HTML: https://mlsanity.com/paper/1606.02555/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [Toward Low-Cost End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2207.00352/) (2022)
- [Pre-training for low resource speech-to-intent applications](https://mlsanity.com/paper/2103.16674/) (2021)
- [Vers la compr'ehension automatique de la parole bout-en-bout `a moindre effort](https://mlsanity.com/paper/2207.00349/) (2022)

## Abstract (arXiv)

In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more traditional Elman and Jordan RNNs. We compare all models, either traditional or new, on four distinct tasks of sequence labeling: two on Spoken Language Understanding (ATIS and MEDIA); and two of POS tagging for the French Treebank (FTB) and the Penn Treebank (PTB) corpora. The results show that our new variants of RNNs are always more effective than the others.