# Latent Sequence Decompositions

arXiv: https://arxiv.org/abs/1610.03035 · HTML: https://mlsanity.com/paper/1610.03035/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2016.

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

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition](https://mlsanity.com/paper/2104.09106/) (2021)
- [CompILE: Compositional Imitation Learning and Execution](https://mlsanity.com/paper/1812.01483/) (2018)
- [Do End-to-End Speech Recognition Models Care About Context?](https://mlsanity.com/paper/2102.09928/) (2021)
- [SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation](https://mlsanity.com/paper/2307.16400/) (2023)

## Abstract (arXiv)

We present the Latent Sequence Decompositions (LSD) framework. LSD decomposes sequences with variable lengthed output units as a function of both the input sequence and the output sequence. We present a training algorithm which samples valid extensions and an approximate decoding algorithm. We experiment with the Wall Street Journal speech recognition task. Our LSD model achieves 12.9% WER compared to a character baseline of 14.8% WER. When combined with a convolutional network on the encoder, we achieve 9.6% WER.