# Segmental Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1511.06018 · HTML: https://mlsanity.com/paper/1511.06018/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2015.

## Built on by (most cited first, 17 of 17)

- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [Sequence-to-Sequence Learning with Latent Neural Grammars](https://mlsanity.com/paper/2109.01135/) (2021)
- [Learning Asynchronous and Sparse Human-Object Interaction in Videos](https://mlsanity.com/paper/2103.02758/) (2021)
- [A Masked Segmental Language Model for Unsupervised Natural Language Segmentation](https://mlsanity.com/paper/2104.07829/) (2021)
- [Subword Segmental Language Modelling for Nguni Languages](https://mlsanity.com/paper/2210.06525/) (2022)
- [Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation](https://mlsanity.com/paper/2305.07005/) (2023)
- [Unsupervised Word Segmentation with Bi-directional Neural Language Model](https://mlsanity.com/paper/2103.01421/) (2021)
- [Morphological Processing of Low-Resource Languages: Where We Are and What's Next](https://mlsanity.com/paper/2203.08909/) (2022)
- [Monotonic segmental attention for automatic speech recognition](https://mlsanity.com/paper/2210.14742/) (2022)
- [Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents](https://mlsanity.com/paper/2302.06448/) (2023)
- [Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation](https://mlsanity.com/paper/2403.07567/) (2024)
- [Neural Sequence Segmentation as Determining the Leftmost Segments](https://mlsanity.com/paper/2104.07217/) (2021)
- [Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages](https://mlsanity.com/paper/2110.08415/) (2021)
- [Filtered Semi-Markov CRF](https://mlsanity.com/paper/2311.18028/) (2023)
- [Streaming Structured Inference with Flash-SemiCRF](https://mlsanity.com/paper/2604.18780/) (2026)
- [Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning](https://mlsanity.com/paper/2607.19845/) (2026)
- [Subword Segmental BabyLMs: Learning to Tokenise for Sample-Efficient Pretraining](https://mlsanity.com/paper/2609.01151/) (2026)

## Abstract (arXiv)

We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments. Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent tokens using bidirectional recurrent neural nets, and these "segment embeddings" are used to define compatibility scores with output labels. These local compatibility scores are integrated using a global semi-Markov conditional random field. Both fully supervised training -- in which segment boundaries and labels are observed -- as well as partially supervised training -- in which segment boundaries are latent -- are straightforward. Experiments on handwriting recognition and joint Chinese word segmentation/POS tagging show that, compared to models that do not explicitly represent segments such as BIO tagging schemes and connectionist temporal classification (CTC), SRNNs obtain substantially higher accuracies.