--- title: "Marie-Catherine de Marneffe — papers, page 2" description: "Marie-Catherine de Marneffe, Munich Center for Machine Learning: 35 papers on Elamur Research (2006–2026), h-index 31 — affiliations…" canonical: "https://research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Marie-Catherine de Marneffe](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) / page 2 # Marie-Catherine de Marneffe · page 2 35 papers in our library · 2006–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) [1](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) **2** 26. **[Not a Simple Yes or No: Uncertainty in Indirect Answers](https://md.research.elamur.ai/free/en/papers/marneffe-2009-not-simple-yes-or-no/)** *(2009 · 23 citations)* Answers to yes/no questions are often indirect. Sometimes the reply only makes "yes" or "no" more or less likely without settling the question. Earlier plan-based, purely logical models, such as Green and Carberry's, cannot weigh such uncertain or conflicting evidence. The authors study these indirect question–answer… 27. **[He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://md.research.elamur.ai/free/en/papers/jiang-2021-he-thinks-he-knows-better/)** *(2021 · 21 citations)* The task is to predict, on a scale from −3 to +3, how factual an embedded event is. The authors fine-tune BERT on seven English datasets, among them FactBank, MEANTIME, UW, UDS-IH2, CommitmentBank (CB), Ross and Pavlick (RP) and MegaVeridicality. BERT does well on most of them, and sets new results on FactBank and… 28. **[Ecologically Valid Explanations for Label Variation in NLI](https://md.research.elamur.ai/free/en/papers/jiang-2023-ecologically-valid-explanations-label-variation/)** *(2023 · 19 citations)* NLI datasets usually assume one correct label per premise–hypothesis pair, yet annotators often disagree, and it has been unclear where that disagreement comes from. 29. **[Multi-word expressions in textual inference: Much ado about nothing?](https://md.research.elamur.ai/free/en/papers/marneffe-2009-multi-word-expressions-textual-inference/)** *(2009 · 12 citations)* The paper asks whether multi-word expressions (MWEs) matter for recognising textual entailment (RTE). It uses the manual Microsoft Research word alignments for the RTE2 data and counts the MWEs that appear as multi-word alignment links. Only 81 (dev) and 60 (test) true MWEs occur, in 8.2% of the 1,600 sentence pairs… 30. **[LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/hong-2025-litex-linguistic-taxonomy-explanations-understanding/)** *(2025 · 8 citations)* Research on NLI disagreement usually studies annotators who choose different labels. This paper targets a less visible case: annotators who pick the same label but justify it in different ways. Highlighted spans, the usual structured form of explanation, cannot show this. Identical spans can hide different arguments… 31. **[Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation](https://md.research.elamur.ai/free/en/papers/jiang-2023-understanding-predicting-human-label-variation/)** *(2023 · 8 citations)* Annotators often disagree on Natural Language Inference (NLI) labels, and this disagreement is systematic rather than random noise. 32. **[Contextualized Embeddings for Enriching Linguistic Analyses on Politeness](https://md.research.elamur.ai/free/en/papers/aljanaideh-2020-contextualized-embeddings-enriching-linguistic-analyses/)** *(2020 · 8 citations)* Politeness studies count words such as please, you or hedges regardless of how they are used. Here every occurrence of a word in the Stanford Politeness Corpus (Wikipedia and Stack Exchange requests, polite/impolite quartiles) gets a contextual BERT vector, and the occurrences are split by a decision-tree-like… 33. **[Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations](https://md.research.elamur.ai/free/en/papers/hong-2026-agree-disagree-explain-decomposing-human/)** *(2026 · 3 citations)* NLI datasets often contain conflicting labels for the same premise–hypothesis pair. Earlier work with the LiTEx taxonomy studied only one kind of variation: annotators who pick the same label but justify it differently. This paper extends LiTEx to cases where the labels themselves diverge. One trained annotator… 34. **[Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/kulmizev-2026-label-explanation-variation-llm-based/)** *(2026 · 2 citations)* The paper asks whether LLMs used as annotators reproduce human label variation (HLV), the genuine disagreement that people show on ambiguous items, or whether they produce a false consensus. 35. **[Evaluative adjective sentences: A question-based analysis of projection](https://md.research.elamur.ai/free/en/papers/tonhauser-2020-evaluative-adjective-sentences-question-based/)** *(2020 · 2 citations)* In "Kim was smart to watch the movie", the prejacent (Kim watched the movie) is usually analysed as a lexical presupposition. [← prev](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) [1](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) **2**