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# Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories

**Authors:** [Sina Zarrieß](https://md.mova.elamur.ai/free/en/docs/papers/authors/sina-zarries/), David Schlangen · **Year:** 2019 · **Venue:** Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pages 654–659 **Link:** [aclanthology.org/P19-1063](https://aclanthology.org/P19-1063/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**15** citations](https://openalex.org/works?filter=cites:W2951064641) openalex-snapshot · 2026-10-14[**9** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/zarrie-2019-know-what-you-don-t/) of the 140673 papers whose bibliographies we read[Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/P19-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/P19-1063/)
- [PDF](https://aclanthology.org/P19-1063.pdf)
- [DOI 10.18653/v1/p19-1063](https://doi.org/10.18653/v1/p19-1063)
- [OpenAlex](https://openalex.org/W2951064641)

**Authors (2)**

1. [Sina Zarrieß](https://openalex.org/A5078051602) [ORCID](https://orcid.org/0000-0002-1384-1218) · h 13 · 479 citations · 161 works
2. [David Schlangen](https://openalex.org/A5032801642) [ORCID](https://orcid.org/0000-0002-2686-6887) · h 28 · 3044 citations · 255 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

A speaker must refer to an object in an image whose category the speaker does not know, for example a bus when only cars were seen in training. A neural literal speaker trained on known categories tends to name the wrong category. The paper adds a pragmatic decoder in the style of Rational Speech Acts: the listener keeps a belief over the object's category, and the speaker chooses each word by a score combining the literal speaker and the listener, with weights that favour words not yet used. Uncertainty is set to uniform over the categories. Six medium-frequency categories (cat, horse, cup, bottle, bus, train) are held out from the RefCOCO training data. In Table 1, the share of expressions that contain a wrong category noun falls for all six categories: for example from 39.8% to 16.6% for bottle and from 62.7% to 33.2% for cup. The share of expressions with no noun at all rises, from 27.5% to 56.2% for bottle. The reference-resolution test uses a listener that knows the true categories, to avoid training the evaluator on the same mistakes. The results are mixed across the two test sets; the authors call them positive evidence for communicative success.

## Ideas

- I1. `prag/src/rsa.rs`: a greedy word-level pragmatic speaker with two fixed weights, one for the literal score and one for the listener score, and a penalty that grows for words already used in the sentence. Keep the score table in a `BTreeMap` and check that two runs give the same text.
- I2. Hedging for unknown words: when a noun in the input is outside the dictionary (`en/src/dict.rs`), prefer a general word ("thing") plus attributes, and count how often a wrong category name is produced for such objects in the school texts.
- I3. Evaluate with a listener that knows the true categories (a rule-based checker such as `coref/src/eval.rs` style scoring, not a model trained on the same text), as the paper does. This avoids the circularity the authors describe. Report resolution accuracy with the reference speaker as a negative control.
- I4. Build a zero-shot split for the school lessons: hold out one set of object words from the training lessons, and test whether the answer avoids them. Split by lesson, not by sentence, and report on the held-out lessons only.

## For Mova

The pragmatic speaker belongs with `prag/src/rsa.rs` and `prag/src/roles.rs`. Unknown-word handling relates to `en/src/dict.rs` and [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/). Reference and the choice of noun belong in [35. Reference and coreference](https://md.mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/), and the evaluation idea fits `coref/src/eval.rs`. Measures: the wrong-category rate and the no-noun rate on held-out lessons, the resolution accuracy with a listener that knows the categories, run twice for determinism.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/p19-1063](https://doi.org/10.18653/v1/p19-1063)

```
@inproceedings{zarries2019know,
  title = {{Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories}},
  author = {Zarrieß, Sina and Schlangen, David},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1063},
  url = {https://doi.org/10.18653/v1/p19-1063}
}
```

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## References

23 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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