---
title: "A Sensitivity Analysis of — Zhang 2017"
description: "One-layer CNNs, such as Kim's (2014) model, are strong and fast baselines for sentence classification."
authors: ["Ye Zhang", "Byron Wallace"]
year: 2017
venue: "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)"
source: "https://aclanthology.org/I17-1026/"
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/zhang-2017-sensitivity-analysis-practitioners-guide-convolutional/"
---
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# A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

**Authors:** Ye Zhang, [Byron Wallace](https://md.research.elamur.ai/free/en/authors/byron-wallace/) · **Year:** 2017 · **Venue:** Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/I17-1026/) · [PDF](https://aclanthology.org/I17-1026.pdf) · [OpenAlex](https://openalex.org/W2216973458)

**Citations:** 150 citations (openalex-snapshot · 2026-10-14) · [104](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
One-layer CNNs, such as Kim's (2014) model, are strong and fast baselines for sentence classification. They still require many architectural and hyperparameter choices, and practitioners have had little evidence about which of those choices actually matter. Zhang and Wallace address this gap with a systematic sensitivity analysis. They fix a baseline configuration, change one component at a time (input embeddings, filter region size, number of feature maps, activation, pooling, dropout, l2 norm constraint) and measure the effect on nine sentence classification datasets. Because CNN training is highly stochastic, each setting is evaluated with ten repetitions of 10-fold cross-validation, and the authors report both means and ranges. The analysis shows that some settings deserve tuning, chiefly filter region size, feature map count and embeddings. Others, such as pooling, have a choice that wins across datasets, and heavy regularization rarely helps. The resulting practical guide gives sensible defaults and search ranges, which are also useful as starting bounds for random search or Bayesian optimization.

## Key points
- **Datasets:** nine in total: Irony (16,006 reddit sentences), Opi (Opinosis, 51 topics of roughly 100 sentences each), plus CR, TREC, Subj, MPQA, MR and the two Stanford Sentiment Treebank variants, SST-1 and SST-2.
  - Irony was balanced by under-sampling negatives and is scored with AUC instead of accuracy.
  - SST models were trained and tested on sentences only, not phrases.
- **Baseline configuration:**
  - Google word2vec embeddings (300-dimensional)
  - region sizes (3,4,5) with 100 feature maps each
  - ReLU activation
  - 1-max pooling
  - dropout 0.5
  - l2 norm constraint of 3
- **Region size and pooling:** region size can strongly affect results; a line search over about 1–10 is suggested. 1-max pooling outperformed the other pooling strategies on every dataset.
- **Regularization:** dropout gave little benefit, both at the softmax layer and on the convolution layer, and high rates hurt badly. The l2 constraint mostly did not help (except on Opi) and harmed CR.
- **Embeddings:** word2vec and GloVe (840B-token web corpus) each win on different tasks. Both beat one-hot inputs.

## Interesting ideas
- **Repeat cross-validation and report ranges:** rerunning the whole CV procedure several times exposes how much a score moves due to random initialization, dropout and SGD alone. This applies to any stochastic model and prevents over-reading small differences between configurations.
- **One-factor-at-a-time sweeps from a fixed baseline:** this cheaply separates the knobs worth tuning from those that barely matter. It is a useful first step before setting up search ranges for a more expensive optimizer.
- **Find the best single filter size, then combine its neighbours:** sets of several sizes close to the best single size consistently beat that size alone. The trick generalizes to other n-gram-window style models.
- **Watch the edge of the search range:** if the best number of feature maps lands near the upper limit (e.g., 600), extend the range. When enlarging capacity starts to hurt, raise dropout. This is a simple diagnostic for any capacity hyperparameter.
- **Balance a skewed dataset by under-sampling and evaluate with AUC:** the authors found under-sampling worked better than over-sampling for the imbalanced Irony data. This is relevant to rare-class detection tasks.

*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

104 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 97 not yet.

- [CVAE-based Re-anchoring for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/dou-2021-cvae-based-re-anchoring-implicit/)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://md.research.elamur.ai/free/en/papers/li-2018-textbugger-generating-adversarial-text-against/)
- [Transformation Networks for Target-Oriented Sentiment Classification](https://md.research.elamur.ai/free/en/papers/li-2018-transformation-networks-target-oriented-sentiment/)
- [A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets](https://md.research.elamur.ai/free/en/papers/mhamed-2022-deep-cnn-architecture-novel-pooling/)
- [Neural-based Context Representation Learning for Dialog Act Classification](https://md.research.elamur.ai/free/en/papers/ortega-2017-neural-based-context-representation-learning/)
- [Shallow Discourse Parsing Using Convolutional Neural Network](https://md.research.elamur.ai/free/en/papers/qin-2016-shallow-discourse-parsing-convolutional-neural/)
- [SGM: Sequence Generation Model for Multi-label Classification](https://md.research.elamur.ai/free/en/papers/yang-2018-sgm-sequence-generation-model-multi/)

## Cite

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

```bibtex
@inproceedings{zhang2017sensitivity,
  title = {{A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification}},
  author = {Zhang, Ye and Wallace, Byron},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2017},
  url = {https://aclanthology.org/I17-1026/}
}
```

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