---
title: "Deep Unordered Composition Rivals Syntactic — Iyyer 2015"
description: "The paper asks whether text classification really needs costly syntax-aware composition, such as recursive or convolutional networks over parse trees and…"
authors: ["Mohit Iyyer", "Varun Manjunatha", "Jordan Boyd-Graber", "Hal Daumé III"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/P15-1162/"
doi: "10.3115/v1/p15-1162"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/iyyer-2015-deep-unordered-composition-rivals-syntactic/"
---
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# Deep Unordered Composition Rivals Syntactic Methods for Text Classification

**Authors:** [Mohit Iyyer](https://md.research.elamur.ai/free/en/authors/mohit-iyyer/), Varun Manjunatha, [Jordan Boyd-Graber](https://md.research.elamur.ai/free/en/authors/jordan-boyd-graber/), [Hal Daumé III](https://md.research.elamur.ai/free/en/authors/hal-daume-iii/) · **Year:** 2015 · **Venue:** Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P15-1162/) · [PDF](https://aclanthology.org/P15-1162.pdf) · [DOI 10.3115/v1/p15-1162](https://doi.org/10.3115/v1/p15-1162) · [OpenAlex](https://openalex.org/W2250473257)

**Citations:** 714 citations (openalex-snapshot · 2026-10-14) · [200](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks whether text classification really needs costly syntax-aware composition, such as recursive or convolutional networks over parse trees and word order, or whether nonlinear depth matters more. The authors propose the deep averaging network (DAN). It averages the word embeddings of a text, passes that average through several feed-forward nonlinear layers, and classifies the result with a softmax. They also introduce word dropout, a regularizer that removes whole tokens from the average during training.

DANs are tested on sentence- and document-level sentiment analysis and on factoid question answering. The comparisons include shallow NBOW baselines and syntactic models such as RecNN, RecNTN and CNNs. The DAN gets close to state-of-the-art accuracy and clearly beats shallow bag-of-words models. It also uses out-of-domain training data with different syntax better than RecNNs, and it trains in minutes on a single laptop core. Error analysis shows that syntactic models fail on largely the same difficult inputs, such as double negation.

The result offers a cheap, strong baseline. It also suggests that, for these tasks, pretrained embeddings and depth matter more than modelling syntax.

## Key points
- **Architecture and cost.** A DAN is an embedding average, followed by one or more `f(W·z + b)` layers, followed by a softmax.
  - Its cost grows with the number of layers, not with the number of parse-tree nodes.
  - Its training time shows no significant difference from shallow NBOW.
  - In preliminary tests, averaging worked better than summing embeddings.
- **Word dropout.** Each token is kept or dropped by an independent Bernoulli draw before averaging. Applying the same idea to RecNN leaf nodes brought no significant gain for sentiment and slightly hurt QA.
- **Pretrained embeddings matter.** Random initialization made DAN converge more slowly and to a worse solution, about 3% lower absolute accuracy on coarse-grained SST. Convolutional and recursive networks show the same effect.
- **Embeddings already encode sentiment.** The authors probed raw 300-dimensional GloVe vectors (Common Crawl) with logistic regression on the Hu and Liu (2004) lexicon (2,006 positive and 4,783 negative words). With a balanced 3,000/1,000 train/test split, accuracy exceeded 95%.
- **Code.** Available at github.com/miyyer/dan.

## Interesting ideas
- **Token-level dropout as cheap augmentation.** Randomly removing whole words before pooling turns each training example into many variants. This could help small labelled Ukrainian datasets where overfitting is a risk.
- **Probing embeddings with a lexicon.** Training a simple linear classifier on lexicon words is a quick way to check whether pretrained vectors already carry a property such as sentiment. The same test could vet Ukrainian embeddings before building a downstream model.
- **Separating nonlinearity from composition.** Comparing a deep unordered model against syntactic ones shows how much of the gain actually comes from syntax. This makes a useful sanity baseline before investing in parser-dependent pipelines, especially for languages with less reliable parsers.
- **Cascading cheap and expensive models.** The authors suggest using a fast DAN by default and calling a slower syntactic model only when the DAN's confidence is low. This design could save compute in large-scale classification of trade or news data.
- **Robustness to syntactic mismatch.** Because a DAN does not depend on parse trees, it can mix training data whose syntax differs from the test data, as in the QA experiments. This is attractive when in-domain labelled data is scarce.

*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

200 papers of our library cite it (of the 140884 whose bibliographies we read); 20 of them have a page here, 180 not yet.

- [Many Languages, One Parser](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/)
- [Generating Sentences from a Continuous Space](https://md.research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/)
- [Universal Sentence Encoder for English](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/)
- [Universal Sentence Encoder](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/dai-2020-analysis-simple-data-augmentation-named/)
- [Probing for semantic evidence of composition by means of simple classification tasks](https://md.research.elamur.ai/free/en/papers/ettinger-2016-probing-semantic-evidence-composition-means/)
- [Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks](https://md.research.elamur.ai/free/en/papers/huang-2019-unicoder-universal-language-encoder-pre/)
- [Neural Sarcasm Detection using Conversation Context](https://md.research.elamur.ai/free/en/papers/jaiswal-2020-neural-sarcasm-detection-conversation-context/)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://md.research.elamur.ai/free/en/papers/kang-2018-dataset-peer-reviews-peerread-collection/)
- [A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-simple-accurate-syntax-agnostic-neural/)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://md.research.elamur.ai/free/en/papers/narrative-2016-kiperwasser-simple-accurate-dependency-parsing-bidirectional/)
- [A Transformer-based approach to Irony and Sarcasm detection](https://md.research.elamur.ai/free/en/papers/potamias-2019-transformer-based-approach-irony-sarcasm/)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://md.research.elamur.ai/free/en/papers/provilkov-2020-bpe-dropout-simple-effective-subword/)
- [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://md.research.elamur.ai/free/en/papers/shen-2018-baseline-needs-more-love-simple/)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://md.research.elamur.ai/free/en/papers/ud-2019-kondratyuk-75-languages-1-model-parsing/)
- [Temporal Reasoning in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/vashishtha-2020-temporal-reasoning-natural-language-inference/)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/zadeh-2017-tensor-fusion-network-multimodal-sentiment/)
- [Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph](https://md.research.elamur.ai/free/en/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/zhang-2017-sensitivity-analysis-practitioners-guide-convolutional/)

## Cite

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

```bibtex
@inproceedings{iyyer2015deep,
  title = {{Deep Unordered Composition Rivals Syntactic Methods for Text Classification}},
  author = {Iyyer, Mohit and Manjunatha, Varun and Boyd-Graber, Jordan and III, Hal Daumé},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-1162},
  url = {https://doi.org/10.3115/v1/p15-1162}
}
```

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