Marie-Catherine de Marneffe · page 2
Papers
- Not a Simple Yes or No: Uncertainty in Indirect Answers (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… - He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics (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… - Ecologically Valid Explanations for Label Variation in NLI (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. - Multi-word expressions in textual inference: Much ado about nothing? (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… - LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference (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… - Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation (2023 · 8 citations)
Annotators often disagree on Natural Language Inference (NLI) labels, and this disagreement is systematic rather than random noise. - Contextualized Embeddings for Enriching Linguistic Analyses on Politeness (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… - Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations (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… - Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference (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. - Evaluative adjective sentences: A question-based analysis of projection (2020 · 2 citations)
In "Kim was smart to watch the movie", the prejacent (Kim watched the movie) is usually analysed as a lexical presupposition.
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