--- title: "Vera Demberg — papers and cards" description: "Vera Demberg: 4 paper cards in the Research library (2012–2024), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/vera-demberg/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/vera-demberg/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Vera Demberg # Vera Demberg 4 paper cards in the Research library · 2012–2024 Vera Demberg wrote 4 of the papers Mova reads, in 2012–2024, most of them on [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/), [Implicature](https://md.research.elamur.ai/free/en/topics/implicature/), [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/); often with [Frances Yung](https://md.research.elamur.ai/free/en/authors/frances-yung/), Merel Scholman, Miriam Kaeshammer. [OpenAlex](https://openalex.org/A5023605306) · [ORCID](https://orcid.org/0000-0002-8834-0020) · [**h-index 28**](https://openalex.org/authors/A5023605306) · [4098 citations](https://openalex.org/authors/A5023605306) · [314 works](https://openalex.org/works?filter=author.id:A5023605306) — openalex-snapshot, 2026-10-14 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=Vera+Demberg) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Vera+Demberg) · [dblp](https://dblp.org/search?q=Vera+Demberg) · [ACL Anthology](https://aclanthology.org/search/?q=Vera+Demberg) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Vera+Demberg) Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) 1 · [Implicature](https://md.research.elamur.ai/free/en/topics/implicature/) 1 · [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/) 1 ## Papers 1. **[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)* RSA-Control steers a pretrained language model towards a target attribute (for example non-toxic, or easy to read) without training a classifier. 2. **[SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives](https://md.research.elamur.ai/free/en/papers/nizamani-2024-siga-naturalistic-nli-dataset-english/)** *(2024)* SIGA is a set of naturally occurring scalar implicatures with gradable adjectives. The authors mine the C4 web corpus for the reinforcement frame «WEAK but not STRONG» («good but not great»), using 88 adjective scale pairs from de Melo & Bansal (2013). The frame guarantees which scale the writer had in mind. Deleting… 3. **[DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/scholman-2022-discogem-crowdsourced-corpus-genre-mixed/)** *(2022)* DiscoGeM holds 6,505 implicit relations between consecutive sentences (no connective among the first five words of the second) from political speeches (Europarl), 20 novels and Wikipedia summaries, each labelled by 10 crowd workers (65,863 judgements) with PDTB 3.0 senses. 4. **[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)* The paper builds a treebank and a lexicon for PLTAG, an incremental, psycholinguistically motivated version of tree-adjoining grammar. ## Co-authors (7) - [Frances Yung](https://md.research.elamur.ai/free/en/authors/frances-yung/) - Merel Scholman - Miriam Kaeshammer - Rashid Nizamani - [Sebastian Schuster](https://md.research.elamur.ai/free/en/authors/sebastian-schuster/) - Tianai Dong - Yifan Wang ## More papers in the library (21) Pages of the Research library: our summary, the ideas, references and who cites each. - [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/) — Iza Škrjanec, Irene Elisabeth Winther, Vera Demberg, et al. · 2026 - [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/) — Soyoung Oh, Xinting Huang, Mathis Pink, et al. · 2026 - [ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer](https://md.research.elamur.ai/free/en/papers/liu-2023-chatgpt-vs-human-authored-text/) — Dongqi Liu, Vera Demberg · 2023 - [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://md.research.elamur.ai/free/en/papers/pyatkin-2023-design-choices-crowdsourcing-implicit-discourse/) — Valentina Pyatkin, Frances Yung, Merel C. J. Scholman, et al. · 2023 - [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/) — Frances Yung, Merel Scholman, Ekaterina Lapshinova-Koltunski, et al. · 2023 - [Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training](https://md.research.elamur.ai/free/en/papers/scholman-2022-design-choices-crowdsourcing-discourse-relation/) — Merel Scholman, Valentina Pyatkin, Frances Yung, et al. · 2022 - [Label distributions help implicit discourse relation classification](https://md.research.elamur.ai/free/en/papers/yung-2022-label-distributions-help-implicit-discourse/) — Frances Yung, Kaveri Anuranjana, Merel Scholman, et al. · 2022 - [Comparison of methods for explicit discourse connective identification across various domains](https://md.research.elamur.ai/free/en/papers/scholman-2021-comparison-methods-explicit-discourse-connective/) — Merel Scholman, Tianai Dong, Frances Yung, et al. · 2021 - [Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2019-acquiring-annotated-data-cross-lingual/) — Wei Shi, Frances Yung, Vera Demberg · 2019 - [Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task](https://md.research.elamur.ai/free/en/papers/yung-2019-crowdsourcing-discourse-relation-annotations-two/) — Frances Yung, Vera Demberg, Merel Scholman · 2019 - [Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2019-learning-explicitate-connectives-seq2seq-network/) — Wei Shi, Vera Demberg · 2019 - [Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains](https://md.research.elamur.ai/free/en/papers/shi-2019-next-sentence-prediction-helps-implicit/) — Wei Shi, Vera Demberg · 2019 - [A Systematic Study of Neural Discourse Models for Implicit Discourse Relation](https://md.research.elamur.ai/free/en/papers/rutherford-2017-systematic-study-neural-discourse-models/) — Attapol Rutherford, Vera Demberg, Nianwen Xue · 2017 - [Examples and Specifications that Prove a Point: Identifying Elaborative and Argumentative Discourse Relations](https://md.research.elamur.ai/free/en/papers/scholman-2017-examples-specifications-that-prove-point/) — Merel C.J. Scholman, Vera Demberg · 2017 - [On the Need of Cross Validation for Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2017-need-cross-validation-discourse-relation/) — Wei Shi, Vera Demberg · 2017 - [Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2017-explicit-discourse-connectives-translation-implicit/) — Wei Shi, Frances Yung, Raphael Rubino, et al. · 2017 - [Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks](https://md.research.elamur.ai/free/en/papers/rehbein-2016-annotating-discourse-relations-spoken-language/) — Ines Rehbein, Merel Scholman, Vera Demberg · 2016 - [Improving event prediction by representing script participants](https://md.research.elamur.ai/free/en/papers/ahrendt-2016-improving-event-prediction-representing-script/) — Simon Ahrendt, Vera Demberg · 2016 - [On the Information Conveyed by Discourse Markers](https://md.research.elamur.ai/free/en/papers/asr-2013-information-conveyed-discourse-markers/) — Fatemeh Torabi Asr, Vera Demberg · 2013 - [Implicitness of Discourse Relations](https://md.research.elamur.ai/free/en/papers/asr-2012-implicitness-discourse-relations/) — Fatemeh Torabi Asr, Vera Demberg · 2012 - [Measuring the Strength of Linguistic Cues for Discourse Relations](https://md.research.elamur.ai/free/en/papers/asr-2012-measuring-strength-linguistic-cues-discourse/) — Fatemeh Torabi Asr, Vera Demberg · 2012