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# Bonnie Dorr

5 paper cards in Mova's library · 1990–2026

Bonnie Dorr wrote 5 of the papers Mova reads, in 1990–2026, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with Fanghua Cao, John Makhoul, Linnea Micciulla.

[OpenAlex](https://openalex.org/A5054060679) · [ORCID](https://orcid.org/0000-0003-4356-5813) · [**h-index 39**](https://openalex.org/authors/A5054060679) · [8547 citations](https://openalex.org/authors/A5054060679) · [432 works](https://openalex.org/works?filter=author.id:A5054060679) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Bonnie+Dorr) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Bonnie+Dorr) · [dblp](https://dblp.org/search?q=Bonnie+Dorr) · [ACL Anthology](https://aclanthology.org/search/?q=Bonnie+Dorr) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Bonnie+Dorr)

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 3

## Papers

1. **[Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety](https://md.mova.elamur.ai/free/en/docs/papers/shen-2026-children-s-english-reading-story/)** *(2026)* Story generators for early readers often break a curriculum's constraints. The authors start from a K–2 phonics curriculum of 129 lessons, each listing its target phonemes, and 2,580 stories (ten per lesson) that GPT-4o and Llama 3.3 70B wrote zero-shot. They fine-tune three 8B models (Llama 3, Apertus, Granite 3.3)…
2. **[A Study of Translation Edit Rate with Targeted Human Annotation](https://md.mova.elamur.ai/free/en/docs/papers/snover-2006-study-translation-edit-rate-targeted/)** *(2006)* The paper proposes Translation Edit Rate (TER): the minimum number of edits (insertion, deletion, substitution, and shift of a word sequence, each costing one) that turns a system output into one of the references, divided by the average reference length.
3. **[CatVar: A Database of Categorial Variations for English](https://md.mova.elamur.ai/free/en/docs/papers/lex-2003-habash-catvar/)** *(2003)* CatVar is a database of categorial variations of English lexemes: a noun, a verb and an adjective that share one root, such as "destroy", "destruction" and "destructive".
4. **[Parameterization of the Interlingua in Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/dorr-1992-parameterization-interlingua-machine-translation/)** *(1992)* The UNITRAN system is an interlingual translator for English, Spanish and German. Its pivot is a lexical conceptual structure shared by all three languages. Differences between languages are not coded as separate processors. They are settings of a few parameters attached to universal principles: for example whether…
5. **[Solving Thematic Divergences in Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/dorr-1990-solving-thematic-divergences-machine-translation/)** *(1990)* A divergent predicate-argument structure is one where the main verb or its arguments take a different syntactic order in the target language, for example when the theme of a verb surfaces as the subject in Spanish but as the object in English.

## Co-authors (10)

- Fanghua Cao
- John Makhoul
- Linnea Micciulla
- Matthew Snover
- Min Yao
- [Nizar Habash](https://md.mova.elamur.ai/free/en/docs/papers/authors/nizar-habash/)
- Qian Shen
- Rich Schwartz
- Shlok Gilda
- Walter Leite
- [← previous Binyam Ephrem Seyoum](https://md.mova.elamur.ai/free/en/docs/papers/authors/binyam-ephrem-seyoum/)
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