---
title: "Information Structure Prediction for Visual-world Referring"
description: "Referring expression generation in visual scenes has mostly studied content selection… 60% F-score. 48 references, cited by 1 paper here."
authors: ["Micha Elsner", "Hannah Rohde", "Alasdair Clarke"]
year: 2014
venue: "Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics"
source: "https://aclanthology.org/E14-1055/"
doi: "10.3115/v1/e14-1055"
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/elsner-2014-information-structure-prediction-visual-world/"
---
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# Information Structure Prediction for Visual-world Referring Expressions

**Authors:** [Micha Elsner](https://md.research.elamur.ai/free/en/authors/micha-elsner/), [Hannah Rohde](https://md.research.elamur.ai/free/en/authors/hannah-rohde/) and Alasdair Clarke · **Year:** 2014 · **Venue:** [Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2014](https://md.research.elamur.ai/free/en/venues/eacl/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/E14-1055/) · [PDF](https://aclanthology.org/E14-1055.pdf) · [DOI 10.3115/v1/e14-1055](https://doi.org/10.3115/v1/e14-1055) · [OpenAlex](https://openalex.org/W2250185474)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Referring expression generation in visual scenes has mostly studied *content selection*, meaning which landmarks to mention. Surface ordering has usually been left to fixed templates. This paper asks what decides the order in which a speaker mentions the target ("anchor") and a helper object ("landmark") in a relational description. The authors use the Mechanical Turk corpus of "Where's Wally" descriptions from Clarke et al. (2013). They annotate each anchor–landmark pair with one of three orderings:

* PRECEDE: the landmark comes first.
* FOLLOW: the landmark comes after the anchor.
* INTER: the landmark sits between pieces of the anchor description.

Each pair also gets an ESTABLISH flag, set when the landmark is first introduced on its own. Image regions are treated separately from real objects. Logistic regression shows that large, visually prominent objects tend to be mentioned early, the way given information is in discourse. A maximum-entropy classifier beats simple baselines, with the clearest gains on non-canonical orders. The main claim is that visual prominence can put an entity into common ground without any prior mention. Template realizers therefore miss real variation in how people order these descriptions.

## Key points
- **Data:** 1672 descriptions from 152 participants, covering 11 Wally scenes with 16 designated targets per scene. Annotations mark which substrings refer to the target, to other objects, and to image regions.
- **Method:** a maxent multilabel classifier, trained with Theano, predicts an (ESTABLISH, direction) pair for each anchor–landmark link.
  - Features include area, salience, distance to the image centre and inter-object distance.
  - These values are discretized into training-set quartiles, which worked better than continuous values.
- **Evaluation setup:** three images were held out for development. Testing was leave-one-image-out over the remaining 8, with training on the other 10 images, giving 2382 test pairs.
- **Results:**
  - Overall pair accuracy rose from 43% (a "regions precede, landmarks follow" baseline) to 52%.
  - Direction accuracy rose from roughly 42–44% to 57%.
  - ESTABLISH reached a 60% F-score, about equal to a lower-bound estimate of inter-annotator agreement.
  - The ceiling from inter-subject agreement is about 74% pair accuracy.
  - All gains over baselines are significant (sign test, p \< 0.01).
- **Negative findings:**
  - Adding the Torralba et al. (2006) visual distinctiveness model did not help.
  - A parser-like structured classifier did no better than independent pair classification, possibly because the description trees are shallow.

## Interesting ideas
- Things that are perceptually or contextually obvious behave as "given" even when nothing has mentioned them, so givenness should not be tied only to earlier text.
- The authors describe a description's structure as an order between object references (precede, follow, interleaved, established first) rather than as word order, which gives a compact and interpretable target to predict.
- Continuous cues are binned into training-set quartiles before a linear model sees them, which captures nonlinear effects cheaply and keeps the features readable.
- Systems are compared against both trivial baselines and a measure of agreement between human speakers, so that 52% can be read against a ceiling of about 74%.
- The authors report honestly that a joint structured model gave no gain over independent pair decisions when the structures were shallow.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): coreference and information-status tagging in prag could treat situationally prominent entities (for example a story's protagonist or a well-known name) as old information, which would help explain why they appear in subject or fronted position.
- **I2** (back): the generator could first choose an ordering strategy for each pair of entities in the world graph, then realize the text, so that every ordering decision can be explained.
- **I3** (learn): perceptron-style models in Mova could quartile-bin numeric features such as distance or length, keeping them human-inspectable while handling nonlinear effects.
- **I4** (learn): Mova's evaluations could report inter-witness or inter-annotator agreement next to negative-control baselines, so that scores have both a floor and a ceiling.
- **I5** (tree): before adding joint or global decoding to gparse-related components, check how deep the target structures really are, because local classifiers may already be enough.

*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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- [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) — University, Edinburgh, United Kingdom · 255 works · 68,771 citations
- [The Ohio State University](https://md.research.elamur.ai/free/en/universities/the-ohio-state-university/) — University, Columbus, United States · 57 works · 17,793 citations

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## Cite

Cite as: Micha Elsner, Hannah Rohde and Alasdair Clarke. 2014. Information Structure Prediction for Visual-world Referring Expressions. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics.

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

```bibtex
@inproceedings{elsner2014information,
  title = {{Information Structure Prediction for Visual-world Referring Expressions}},
  author = {Micha Elsner, Hannah Rohde and Alasdair Clarke},
  booktitle = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics},
  year = {2014},
  doi = {10.3115/v1/e14-1055},
  url = {https://doi.org/10.3115/v1/e14-1055}
}
```

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*Updated 24 October 2026*