---
title: "Intensionalizing Abstract Meaning Representations:"
description: "Abstract Meaning Representation (AMR) encodes who-did-what-to-whom well. 34 references, cited by 3 papers here."
authors: ["Gregor Williamson", "Patrick Elliott", "Yuxin Ji"]
year: 2021
venue: "Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop"
source: "https://aclanthology.org/2021.law-1.17/"
doi: "10.18653/v1/2021.law-1.17"
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/williamson-2021-intensionalizing-abstract-meaning-representations-non/"
---
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# Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope

**Authors:** Gregor Williamson, Patrick Elliott and Yuxin Ji · **Year:** 2021 · **Venue:** [Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop](https://md.research.elamur.ai/free/en/venues/law/) ([LAW 2021](https://md.research.elamur.ai/free/en/venues/law/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.law-1.17/) · [PDF](https://aclanthology.org/2021.law-1.17.pdf) · [DOI 10.18653/v1/2021.law-1.17](https://doi.org/10.18653/v1/2021.law-1.17) · [OpenAlex](https://openalex.org/W3199524447)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/williamson-2021-intensionalizing-abstract-meaning-representations-non/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Abstract Meaning Representation (AMR) encodes who-did-what-to-whom well. But read as a flat conjunction of triples, it mishandles attitude reports: if a sentence says a boy holds the belief that a girl is sick, its AMR lets one conclude that she really is sick just by removing conjuncts. The authors fix this without layered graphs such as GKR. They give a compositional translation from AMR into the Simply-Typed Lambda Calculus (STLC), first extensional and then intensionalized with world-typed propositions. They also add one new role, :content, for propositional arguments. Following Kratzer-style event semantics, :content links an attitude event to a proposition through a modal operator cont, so the embedded clause is never asserted in the actual world. They then combine this with a scope node and Cooper storage to derive de re, de dicto and intermediate-scope readings when quantifiers interact with attitude verbs. The result matters because AMR, and also UMR, gain correct non-veridical semantics at small annotation cost. It also prepares AMR for NLI that uses veridicality lexicons.

## Key points
- PropBank's believe-01 uses :ARG1 for both a believed person ("believed the girl") and a believed clause. So simply marking :ARG1 as non-veridical would wrongly block the inference that the girl exists.
- Inverse roles cause a second problem. Since R(x)(y) ⇔ R-of(y)(x), two AMRs for "The boy believes he is sick" collapse to the same triples. With :content, the variant whose :content points to a re-entrant variable is undefined, because cont needs a full instance assignment.
- :content has type e → (s→t) → (s→t). It applies existential closure inside the embedded proposition. A custom Free function keeps re-entrant variables from being bound before their instance assignment, which avoids continuation-passing style.
- One reading remains out of reach: the non-specific de re reading of "The boy hopes to buy a violin". The authors argue that capturing it would make AMRs too hard for annotators and parsers.
- Converting existing corpora needs only a many-to-one mapping (numbered argument → :content), not a frameset revision. A Python AMR→STLC translator is released at github.com/emorynlp/Intensionalizing-AMR.

## Interesting ideas
- Mark propositional arguments with a dedicated role, so that an attitude event *has* a content rather than the clause being conjoined with the main assertion; truth then never leaks out of belief contexts.
- Tell entity-type and clause-type arguments of the same verb sense apart ("believed the girl" vs "believed that the girl…") instead of relying on a single numbered slot from the frame lexicon.
- Use type well-formedness as a validity check: a content role that points to a bare re-entrant variable is rejected, which also flags graphs that match no coherent sentence.
- Defer existential binding to a final closure step that binds only variables introduced by instance assignments, instead of threading continuations through composition.
- Pair the intensional logical form with factivity lexicons (factive know, counterfactive pretend, non-veridical believe, e.g. MegaVeridicality) for entailment decisions.

## For Mova

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

- **I1** (world): Facts from a complement of believe/hope/claim could be stored as the content of an attitude event in a separate sub-world, not as states of the text's main world.
- **I2** (frames): When mapping VerbNet roles, the prag crate could split the theme of attitude verbs into person and proposition variants, decided by whether the UD dependent is ccomp/xcomp or obj.
- **I3** (learn): Silver frame graphs could be filtered with type constraints like this one, so witness-parser outputs that put an entity where a proposition belongs become automatic negative examples.
- **I4** (world): Entity creation for a sentence could be delayed until the whole frame is built, which makes reflexives and other re-entrant mentions simple variable matches.
- **I5** (answer): QA on fables could answer "Is the girl sick?" with "the boy only believes so" and use the attitude verb's veridicality class to choose between yes, no and unknown.

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

## Institutions

- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 159 works · 65,157 citations
- [Emory University](https://md.research.elamur.ai/free/en/universities/emory-university/) — University, Atlanta, United States · 16 works · 39,535 citations

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🧭 Topics (1): [🧾 Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) · 135 works

## Cite

Cite as: Gregor Williamson, Patrick Elliott and Yuxin Ji. 2021. Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope. Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop.

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

```bibtex
@inproceedings{williamson2021intensionalizing,
  title = {{Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope}},
  author = {Gregor Williamson, Patrick Elliott and Yuxin Ji},
  booktitle = {Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop},
  year = {2021},
  doi = {10.18653/v1/2021.law-1.17},
  url = {https://doi.org/10.18653/v1/2021.law-1.17}
}
```

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*Updated 24 October 2026*