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# Neural Models of Factuality

**Authors:** [Rachel Rudinger](https://md.mova.elamur.ai/free/en/docs/papers/authors/rachel-rudinger/), [Aaron Steven White](https://md.mova.elamur.ai/free/en/docs/papers/authors/aaron-steven-white/), [Benjamin Van Durme](https://md.mova.elamur.ai/free/en/docs/papers/authors/benjamin-van-durme/) · **Year:** 2018 · **Venue:** NAACL 2018 **Link:** [aclanthology.org/N18-1067](https://aclanthology.org/N18-1067/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); the UDS-IH2 annotations sit on the English UD treebank and have their own terms

[**60** citations](https://openalex.org/works?filter=cites:W2796244435) openalex · 2026-10-18[**52** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/rudinger-2018-neural-models-factuality/) of the 140673 papers whose bibliographies we read[Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)](https://aclanthology.org/volumes/N18-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/N18-1067/)
- [PDF](https://aclanthology.org/N18-1067.pdf)
- [DOI 10.18653/v1/n18-1067](https://doi.org/10.18653/v1/n18-1067)
- [OpenAlex](https://openalex.org/W2796244435)

**Authors (3)**

1. [Rachel Rudinger](https://openalex.org/A5082447472) [ORCID](https://orcid.org/0000-0002-5506-4701) · h 20 · 2660 citations · 90 works
2. [Aaron Steven White](https://openalex.org/A5090639239) [ORCID](https://orcid.org/0000-0003-0057-9246) · h 19 · 1185 citations · 69 works
3. [Benjamin Van Durme](https://openalex.org/A5075825791) [ORCID](https://orcid.org/0000-0003-4328-4288) · h 51 · 9972 citations · 392 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Event factuality asks, for each event-denoting predicate, whether the event happened according to the author, on a scale from −3 (certainly not) to +3 (certainly did). The paper extends the «It Happened» annotations to every predicate of the English Web Treebank in UD 1.2 (UDS-IH2: 27,289 predicates, two crowd judgements each, raw agreement 0.84, kappa 0.66), a more varied set than the news-based FactBank, UW and MEANTIME. It reviews what decides factuality: outside context (factives *know*, implicatives *manage*/*fail*, veridicals *prove*, non-veridicals *want*/*hope*, with Nairn et al.'s implication signatures that flip under negation) and inside context (negative determiners: an odd number of *no* arguments makes the event non-factual). Stacked bidirectional LSTMs over the word sequence and over the dependency tree, with features from those signatures and from corpus-mined implicativeness, beat earlier rule-based and feature systems on all four datasets; the linear-chain network is reliably better than the tree one, and multi-task training and an ensemble help.

## Ideas

- I1. Give every frame a factuality value, not only `neg` and `cond`: happened / did not happen / not asserted (wanted, planned, hoped, asked, ordered) / uncertain (modal, hedge). Compute it by propagating the Nairn implication signatures down the tree in `prag/src/frames.rs`: *manage to X* +|−, *fail to X* −|+, *forget to X* −|+, *forget that X* and *know that X* +|+, *want/hope/try to X* ○|○, negation and *never* flip, a negative quantifier in an argument flips. Each frame prints the chain that set its value.
- I2. Let the student remember only factual frames as facts and keep the rest as wishes or plans (`world/src/knowledge.rs`): «The fox wanted to eat the grapes» must not answer «Did the fox eat the grapes?» with yes. The Torah diagnosis already shows negation losing to a positive frame; a factuality value is the cleaner fix than a heavier negation weight. Measure on the courses with `world knowledge diagnose` (step 5, right option outscored) and the course test counts.
- I3. Grow the signature list from the tales with the tense-agreement trick the paper cites (an implicative's complement agrees in tense: «managed to escape» → the escape is in the past): count, per embedding verb, how often its complement event is later narrated as having happened. Our own counts from public-domain tales, reviewed by hand before entering `prag/data`.
- I4. Use UDS-IH2 (on UD English EWT, which Mova already parses) only as a test set for the factuality value of I1, if its licence allows: mean absolute error and correlation against the −3…+3 judgements, with the «always factual» baseline next to it.

## For Mova

[33. Implicature and presupposition](https://md.mova.elamur.ai/free/en/docs/languages/en/33-implicature-and-presupposition/) §6 (factive triggers; Mova's `infer.rs` already checks *know*, *realize*, *regret*, *forget*), `27-negation.md`, `32-politeness-and-hedging.md` §8 (epistemic modality). Code: `prag/src/frames.rs` (factuality value), `prag/src/infer.rs`, `world/src/knowledge.rs`. Measures: course test counts and diagnosis steps; frame bench unchanged or better; UDS-IH2 error as a test only.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/n18-1067](https://doi.org/10.18653/v1/n18-1067)

```
@inproceedings{rudinger2018neural,
  title = {{Neural Models of Factuality}},
  author = {Rudinger, Rachel and White, Aaron Steven and Durme, Benjamin Van},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-1067},
  url = {https://doi.org/10.18653/v1/n18-1067}
}
```

- [← previous How well do NLI models capture verb veridicality?](https://md.mova.elamur.ai/free/en/docs/papers/ross-2019-how-well-do-nli-models/)
- [next → Towards a Heuristic Categorization of Prepositional Phrases in English with WordNet](https://md.mova.elamur.ai/free/en/docs/papers/rudzicz-2010-towards-heuristic-categorization-prepositional-phrases/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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