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# Thematic role assignment in non-default verb classes: A cross-linguistic comparison of English and German

**Authors:** Louise Kyriaki, Matthias Schlesewsky, Ina Bornkessel-Schlesewsky · **Year:** 2021 · **Venue:** Glossa: a journal of general linguistics 6(1) **Link:** [glossa-journal.org/article/id/5728](https://www.glossa-journal.org/article/id/5728/) (DOI [10.16995/glossa.5728](https://doi.org/10.16995/glossa.5728)) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**1** citations](https://openalex.org/works?filter=cites:W3161221743) openalex · 2026-10-18[Glossa: a journal of general linguistics](https://openalex.org/S4210226983)[licence](https://creativecommons.org/licenses/by/4.0/)

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- [DOI 10.16995/glossa.5728](https://doi.org/10.16995/glossa.5728)
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**Authors (3)**

1. [Louise Kyriaki](https://openalex.org/A5037044400) [ORCID](https://orcid.org/0000-0001-9496-9506) · h 3 · 21 citations · 18 works
2. [Matthias Schlesewsky](https://openalex.org/A5069058319) [ORCID](https://orcid.org/0000-0003-4131-8077) · h 46 · 8138 citations · 193 works
3. Ina Bornkessel-Schlesewsky

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

## What it is about

The study asks how English and German speakers assign thematic roles (who is the actor, who is the undergoer) in sentences where the two noun phrases are reversed in animacy, so that word order and animacy point in different directions. Two verb classes are compared: agent-subject verbs ("The men will devour the meals") and experiencer-subject verbs ("The children will enjoy the holidays"). The probe asks for either the actor or the undergoer. The design is a 2 x 2 x 2 x 2 online survey with 38 English and 26 German native speakers, analysed with mixed-effects logistic models. The English speakers chose the first noun as actor most often (86.5% for agent verbs with an animate-first sentence, 70.3% for inanimate-first). German speakers relied more on animacy: 71.7% and 53.6% in the same conditions. Experiencer verbs and undergoer questions both shifted German responses towards animacy. The authors interpret this in the Competition Model: cue weights are not fixed and depend on other cues and on the task.

## Ideas

- I1. Make the role decision a weighted sum of cues in `prag/src/roles.rs`: word order (first NP = actor) and animacy (animate = actor) with weights per verb class. Set the English weights so the word-order cue dominates and check the output on the paper's conditions; each decision should list the cues that voted.
- I2. Verb-class feature from the lexicon: experiencer-subject verbs (*enjoy, fear, like*) lower the word-order weight and raise the animacy weight. Add this as a lexicon field, not a rule over surface strings.
- I3. Question-focus as a reading rule: when the question asks about the undergoer, the first NP becomes the topic and is more likely to be read as the undergoer for inanimate first nouns. For English, use the paper's result that this effect is weak in English, so the rule should be small there.
- I4. Negative control: keep the paper's four conditions and shuffle the NP order. The shuffled version must score worse than the original on the English word-order cue, otherwise the cue weights are not doing their job.

## For Mova

The chapters are [21. The simple clause](https://md.mova.elamur.ai/free/en/docs/languages/en/21-clause-structure/) (SVO as the English role cue), [28. Voice and the passive](https://md.mova.elamur.ai/free/en/docs/languages/en/28-voice-and-passive/) (passive as an alternative when the undergoer is topical) and [29. Ellipsis, word order and information structure](https://md.mova.elamur.ai/free/en/docs/languages/en/29-ellipsis-word-order-information-structure/) (question focus and topicality). The code is `prag/src/roles.rs` and `prag/src/frames.rs` (role assignment), with the animacy values taken from the lexicon in `en/src/dict.rs`. The measure: agreement of the rule-based roles with the paper's percentages on English conditions (the English numbers are the target; the German ones are not for Mova), run twice for determinism, plus the shuffled-order negative control.

Topics: [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.16995/glossa.5728](https://doi.org/10.16995/glossa.5728)

```
@article{kyriaki2021thematic,
  title = {{Thematic role assignment in non-default verb classes: A cross-linguistic comparison of English and German}},
  author = {Kyriaki, Louise and Schlesewsky, Matthias and Bornkessel-Schlesewsky, Ina},
  journal = {Glossa: a journal of general linguistics},
  year = {2021},
  doi = {10.16995/glossa.5728},
  url = {https://doi.org/10.16995/glossa.5728}
}
```

- [← previous QUD-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation](https://md.mova.elamur.ai/free/en/docs/papers/kuthy-2018-qud-based-annotation-discourse-structure/)
- [next → Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds](https://md.mova.elamur.ai/free/en/docs/papers/labutov-2018-multi-relational-question-answering-narratives/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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