---
title: "DiSAN — paper by Shen et al. (2017) — summary and ideas"
description: "The authors propose DiSAN, a lightweight sentence-encoding network that uses only attention. 49 references, cited by 105 papers here."
authors: ["Tao Shen", "Tianyi Zhou", "Guodong Long", "Jing Jiang", "Shirui Pan", "Chengqi Zhang"]
year: 2017
venue: "arXiv:1709.04696"
source: "https://arxiv.org/abs/1709.04696"
doi: "10.48550/arxiv.1709.04696"
arxiv: "1709.04696"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/shen-2017-disan-directional-self-attention-network/"
---
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# DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

**Authors:** [Tao Shen](https://md.research.elamur.ai/free/en/authors/tao-shen/), [Tianyi Zhou](https://md.research.elamur.ai/free/en/authors/tianyi-zhou/), [Guodong Long](https://md.research.elamur.ai/free/en/authors/guodong-long/), [Jing Jiang](https://md.research.elamur.ai/free/en/authors/jing-jiang/), [Shirui Pan](https://md.research.elamur.ai/free/en/authors/shirui-pan/) and [Chengqi Zhang](https://md.research.elamur.ai/free/en/authors/chengqi-zhang/) · **Year:** 2017 · **Venue:** [arXiv:1709.04696](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/) ([Proceedings of the AAAI Conference on Artificial Intelligence 2017](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/2017/)) · **Publisher:** [Association for the Advancement of Artificial Intelligence](https://md.research.elamur.ai/free/en/publishers/association-for-the-advancement-of-artificial-intelligence/) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1709.04696) · [PDF](https://arxiv.org/pdf/1709.04696) · [DOI 10.48550/arxiv.1709.04696](https://doi.org/10.48550/arxiv.1709.04696) · [arXiv 1709.04696](https://arxiv.org/abs/1709.04696) · [OpenAlex](https://openalex.org/W2964189376)

**Citations:** 749 citations (OpenAlex, 2026-10-14) · [105](https://md.research.elamur.ai/free/en/papers/shen-2017-disan-directional-self-attention-network/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Sentence encoders in NLP usually rely on RNNs for long-range context or on CNNs for local patterns. Both have drawbacks: RNNs process tokens sequentially, and standard attention ignores word order. The authors propose DiSAN, a lightweight sentence-encoding network that uses only attention. It has two new components. The first is multi-dimensional attention, which gives each token-pair a score per feature rather than a single scalar. The second is directional self-attention, where positional masks restrict attention to forward or backward context and so encode temporal order. A fusion gate mixes each token's input with its attended context. A final multi-dimensional "source2token" attention then pools the sequence into one vector for downstream classifiers or regressors. Without stacked blocks or an encoder-decoder design, DiSAN posts the best SNLI result among sentence-encoding models and strong results on seven other benchmarks. It also needs fewer parameters and less time than the LSTM and tree-based models it beats. This shows that order-aware attention alone can replace recurrence and convolution for general sentence representation.

## Key points
- Improves the previous best sentence-encoding accuracy on SNLI by 1.02%.
- Reports state-of-the-art test accuracy on SST, MultiNLI, SICK, Customer Review (CR), MPQA, TREC and SUBJ.
- Architecture: forward and backward masked self-attention blocks with a fusion gate, followed by multi-dimensional source2token attention. No RNN or CNN layers are used.
- Heatmaps on SNLI sentences show three patterns:
  - Content words get more attention than stop words.
  - Forward and backward blocks focus on different parts of a sentence.
  - Feature-wise vectors separate different senses of the same word (e.g., "glass", "close").
- Published at AAAI 2018; code and pre-trained models are at github.com/taoshen58/DiSAN.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) — University, Seattle, United States · 402 works · 212,763 citations
- [University of Technology Sydney](https://md.research.elamur.ai/free/en/universities/university-of-technology-sydney/) — University, Sydney, Australia · 19 works · 12,908 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

💡 Concepts (1): [💡 Natural language understanding](https://md.research.elamur.ai/free/en/concepts/natural-language-understanding/) · 52 works

## Cite

Cite as: Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan and Chengqi Zhang. 2017. DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding. arXiv:1709.04696.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{shen2017disan,
  title = {{DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding}},
  author = {Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan and Chengqi Zhang},
  year = {2017},
  doi = {10.48550/arxiv.1709.04696},
  eprint = {1709.04696},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1709.04696}
}
```

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*Updated 2 November 2026*