---
title: "Learning Grounded Meaning Representations with Autoencoders"
description: "Silberer and Lapata describe each concrete noun by two automatically obtained attribute vectors, one… 55 references, cited by 70 papers here."
authors: ["Carina Silberer", "Mirella Lapata"]
year: 2014
venue: "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P14-1068/"
doi: "10.3115/v1/p14-1068"
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/silberer-2014-learning-grounded-meaning-representations-autoencoders/"
---
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# Learning Grounded Meaning Representations with Autoencoders

**Authors:** [Carina Silberer](https://md.research.elamur.ai/free/en/authors/carina-silberer/) and [Mirella Lapata](https://md.research.elamur.ai/free/en/authors/mirella-lapata/) · **Year:** 2014 · **Venue:** [Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2014](https://md.research.elamur.ai/free/en/venues/acl/2014/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **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/P14-1068/) · [PDF](https://aclanthology.org/P14-1068.pdf) · [DOI 10.3115/v1/p14-1068](https://doi.org/10.3115/v1/p14-1068) · [OpenAlex](https://openalex.org/W2250742840)

**Citations:** 217 citations (OpenAlex, 2026-10-14) · [70](https://md.research.elamur.ai/free/en/papers/silberer-2014-learning-grounded-meaning-representations-autoencoders/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles grounding distributional word meaning in perception. Text-only vector spaces ignore the physical world, and earlier multimodal models often just glue together text and image vectors that have very different statistical properties. Silberer and Lapata describe each concrete noun by two automatically obtained attribute vectors, one inferred from text and one predicted from images. The two sources come from separate data, not paired documents. Each modality first goes through its own stacked denoising autoencoder. A joint layer then fuses them into a bimodal embedding. Fine-tuning is semi-supervised: the network reconstructs the attribute input and also predicts the object's class through a softmax. The embeddings are tested on word similarity, using a new set of 7,576 noun pairs rated for both semantic and visual similarity, and on concept categorization with Chinese Whispers clustering. The bimodal SAE beats SVD, kCCA, Bruni et al. and an RNN language model on both tasks. This suggests that learned higher-level attribute embeddings capture complementary textual and visual knowledge.

## Key points
- **New similarity dataset:** 7,576 word pairs, each with separate human ratings for semantic and visual similarity. Unlike WordSim353, this lets each modality be studied on its own.
- **Similarity results:** the bimodal SAE (T+V) achieves ρ = 0.70 for semantic similarity and ρ = 0.64 for visual similarity, compared with inter-annotator agreement of 0.76 and 0.63, respectively. The SAE outperforms all other bimodal models.
- **Unimodal SAEs behave as expected:**
  - The textual SAE is stronger on semantic similarity (0.65 vs 0.60 for the visual SAE).
  - The visual SAE is stronger on visual similarity (0.60 vs 0.52 for the textual SAE).
- **Categorization F-scores (T+V):**
  - SAE 0.43, SVD 0.39, kCCA 0.37, Bruni 0.34.
  - Simple attribute concatenation scores 0.33; RNN-640 (text) scores 0.32.
  - The SAE comes close to the human McRae norms (0.42).
- **Models without attributes trail behind:** Bruni et al. and the Mikolov RNN are outperformed by every attribute-based system on similarity.

## Interesting ideas
- **Use attributes as a shared interface between modalities.** Expressing both image and text information as natural-language attributes puts them in a common, interpretable space before fusion. This could help merge heterogeneous sources, such as geographic or trade data, with text.
- **Train modalities on decoupled data.** Text and image attributes come from separate resources, linked only by the word. Fusion therefore does not require aligned multimodal corpora, which are often hard to obtain for lower-resource languages.
- **Combine reconstruction with a classification signal.** Adding object-class prediction to the autoencoder's reconstruction objective is an inexpensive form of weak supervision. It pushes embeddings to be task-relevant without abandoning unsupervised structure.
- **Collect ratings per modality.** Separate semantic and visual similarity judgments let you diagnose what each input source contributes. The same design fits any evaluation of multi-source embeddings.
- **Build each modality's representation before fusing.** Even the unimodal SAEs usually beat the raw attribute vectors. Learning a compressed encoding for each source first may pay off in purely textual NLP pipelines too.

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

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## Cite

Cite as: Carina Silberer and Mirella Lapata. 2014. Learning Grounded Meaning Representations with Autoencoders. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{silberer2014learning,
  title = {{Learning Grounded Meaning Representations with Autoencoders}},
  author = {Carina Silberer and Mirella Lapata},
  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2014},
  doi = {10.3115/v1/p14-1068},
  url = {https://doi.org/10.3115/v1/p14-1068}
}
```

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*Updated 24 October 2026*