--- title: "Vera Demberg — papers, page 2 — Elamur Research" description: "Vera Demberg, Max Planck Institute for Informatics: 33 papers on Elamur Research (2012–2026), h-index 28 — affiliations, topics, co-authors…" canonical: "https://research.elamur.ai/free/en/authors/vera-demberg/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/vera-demberg/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Vera Demberg](https://md.research.elamur.ai/free/en/authors/vera-demberg/) / page 2 # Vera Demberg · page 2 33 papers in our library · 2012–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/vera-demberg/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/vera-demberg/) [1](https://md.research.elamur.ai/free/en/authors/vera-demberg/) **2** 26. **[Investigating Explicitation of Discourse Connectives in Translation using Automatic Annotations](https://md.research.elamur.ai/free/en/papers/yung-2023-investigating-explicitation-discourse-connectives-translation/)** *(2023 · 8 citations)* Translators frequently insert, drop, or rework discourse connectives such as because or however , and earlier studies found a general drift toward explicitation. 27. **[German and English Treebanks and Lexica for Tree-Adjoining Grammars](https://md.research.elamur.ai/free/en/papers/kaeshammer-2012-german-english-treebanks-lexica-tree/)** *(2012 · 8 citations)* The paper builds a treebank and a lexicon for PLTAG, an incremental, psycholinguistically motivated version of tree-adjoining grammar. 28. **[RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework](https://md.research.elamur.ai/free/en/papers/wang-2024-rsa-control-pragmatics-grounded-lightweight/)** *(2024 · 7 citations)* RSA-Control steers a pretrained language model towards a target attribute (for example non-toxic, or easy to read) without training a classifier. 29. **[Rollenwechsel-English: a large-scale semantic role corpus](https://md.research.elamur.ai/free/en/papers/sayeed-2018-rollenwechsel-english-large-scale-semantic/)** *(2018 · 6 citations)* The authors build a very large English resource of automatically labelled semantic frames, aimed at modelling generalized event knowledge, meaning how typical a noun is as, say, the agent or instrument of a verb. 30. **[Modeling Turn-Taking with Semantically Informed Gestures](https://md.research.elamur.ai/free/en/papers/suresh-2026-modeling-turn-taking-semantically-informed/)** *(2026 · 1 citation)* Computational turn-taking models usually rely on speech content and prosody and ignore body movement. This paper asks whether meaning-bearing gestures add useful signal for predicting whether a speaker will keep or give up the floor in multi-party conversation. The authors extend the DnD Gesture corpus, which… 31. **[Tug-of-war between idioms' figurative and literal interpretations in LLMs](https://md.research.elamur.ai/free/en/papers/oh-2026-tug-war-between-idioms-figurative/)** *(2026 · 1 citation)* The paper asks how autoregressive transformers handle idioms, which can be read both figuratively (e.g., "to die") and literally (e.g., physically kicking a bucket). 32. **[Is Cross-Lingual Transfer in Bilingual Models Human-Like? A Study with Overlapping Word Forms in Dutch and English](https://md.research.elamur.ai/free/en/papers/skrjanec-2026-is-cross-lingual-transfer-bilingual/)** *(2026)* Bilingual readers process words that look the same in both languages differently. Cognates such as winter tend to speed up reading. Interlingual homographs such as Dutch brand ("fire") either slow reading down or have no effect. This paper asks whether bilingual language models show the same pattern. 33. **[Barch: an English Dataset of Bar Chart Summaries](https://md.research.elamur.ai/free/en/papers/skrjanec-2022-barch-english-dataset-bar-chart/)** *(2022)* Chart-to-text generation needs charts paired with descriptions, but existing resources have problems. AutoChart uses template text of doubtful naturalness. Chart2Text uses crawled human text that often brings in outside knowledge, and its alignment skips inferential statements. Barch addresses this with human-written… [← prev](https://md.research.elamur.ai/free/en/authors/vera-demberg/) [1](https://md.research.elamur.ai/free/en/authors/vera-demberg/) **2**