---
title: "Hierarchical Attention Networks for Document — Yang 2016"
description: "The paper looks at document classification, where a model must assign labels such as sentiment ratings or topics to long texts."
authors: ["Zichao Yang", "Diyi Yang", "Chris Dyer", "Xiaodong He", "Alex Smola", "Eduard Hovy"]
year: 2016
venue: "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/N16-1174/"
doi: "10.18653/v1/n16-1174"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/"
---
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# Hierarchical Attention Networks for Document Classification

**Authors:** [Zichao Yang](https://md.research.elamur.ai/free/en/authors/zichao-yang/), [Diyi Yang](https://md.research.elamur.ai/free/en/authors/diyi-yang/), [Chris Dyer](https://md.research.elamur.ai/free/en/authors/chris-dyer/), [Xiaodong He](https://md.research.elamur.ai/free/en/authors/xiaodong-he/), [Alex Smola](https://md.research.elamur.ai/free/en/authors/alex-smola/), [Eduard Hovy](https://md.research.elamur.ai/free/en/authors/eduard-hovy/) · **Year:** 2016 · **Venue:** Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N16-1174/) · [PDF](https://aclanthology.org/N16-1174.pdf) · [DOI 10.18653/v1/n16-1174](https://doi.org/10.18653/v1/n16-1174) · [OpenAlex](https://openalex.org/W2470673105)

**Citations:** 4712 citations (openalex-snapshot · 2026-10-14) · [697](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper looks at document classification, where a model must assign labels such as sentiment ratings or topics to long texts. Earlier neural models usually read a document as one flat sequence. The authors propose the Hierarchical Attention Network (HAN), which follows the natural structure of a document. A bidirectional GRU first encodes the words of each sentence. A learned word-level context vector then scores each word, and the weighted words form a sentence vector. A second bidirectional GRU encodes these sentence vectors. A sentence-level attention layer combines them into one document vector, which a softmax classifier labels. Because each word is weighted in its context, the same word can matter more in one document than in another. HAN was tested on six large datasets: three Yelp review sets, IMDB, Yahoo Answers and Amazon. It beat linear models, SVMs, CNNs, LSTMs and the hierarchical Conv-GRNN and LSTM-GRNN baselines by a wide margin. The attention weights can also be visualised to show which words and sentences drove each decision.

## Key points
- **Architecture:** HAN has four parts: a word encoder (BiGRU), word attention, a sentence encoder (BiGRU) and sentence attention. Both attention layers score importance against a context vector that starts random and is learned during training. The authors describe this as the first hierarchical attention mechanism.
- **Data:** The datasets are Yelp 2013/2014/2015 (ratings 1–5), IMDB (ratings 1–10), Yahoo Answers (10 topic classes) and Amazon (ratings 1–5; 3,000,000 training and 650,000 test reviews). Most sets were split 80/10/10 into training, validation and test data.
- **Baselines:** BOW and n-gram models (with and without TFIDF), bag-of-means, SVM variants (including SSWE and AverageSG), word-level and character-level CNNs, LSTM, Conv-GRNN and LSTM-GRNN. These results come from Zhang et al. (2015) and Tang et al. (2015).
- **Interpretability:** On Yelp reviews, the attention picks out strongly emotional words such as "delicious" and "terrible". It also gives little weight to sentences that sound negative on their own but are positive in context. On Yahoo Answers it highlights words that point to the topic, such as "zebra" and "camouflage".
- **Missing numbers:** The provided text skipped the middle of the paper, including the results tables, so this summary has no per-dataset accuracy figures.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

697 papers of our library cite it (of the 140884 whose bibliographies we read); 48 of them have a page here, 649 not yet.

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- [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/)
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## Cite

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

```bibtex
@inproceedings{yang2016hierarchical,
  title = {{Hierarchical Attention Networks for Document Classification}},
  author = {Yang, Zichao and Yang, Diyi and Dyer, Chris and He, Xiaodong and Smola, Alex and Hovy, Eduard},
  booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2016},
  doi = {10.18653/v1/n16-1174},
  url = {https://doi.org/10.18653/v1/n16-1174}
}
```

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- [next → How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature](https://md.research.elamur.ai/free/en/papers/joshi-2016-how-challenging-is-sarcasm-versus/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/2/)

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