PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments
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Abstract (arXiv)
What makes a presupposition of an utterance -- information taken for granted by its speaker -- different from other pragmatic inferences such as an entailment is projectivity (e.g., the negative sentence the boy did not stop shedding tears presupposes the boy had shed tears before). The projectivity may vary depending on the combination of presupposition triggers and environments. However, prior natural language understanding studies fail to take it into account as they either use no human baseline or include only negation as an entailment-canceling environment to evaluate models' performance. The current study attempts to reconcile these issues. We introduce a new dataset, projectivity of presupposition (PROPRES, which includes 12k premise-hypothesis pairs crossing six triggers involving some lexical variety with five environments. Our human evaluation reveals that humans exhibit variable projectivity in some cases. However, the model evaluation shows that the best-performed model, DeBERTa, does not fully capture it. Our findings suggest that probing studies on pragmatic inferences should take extra care of the human judgment variability and the combination of linguistic items.
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References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
- 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 876
- 2018Annotation Artifacts in Natural Language Inference Datacited by 671
- 2017Supervised Learning of Universal Sentence Representations from Natural Language Inference Datacited by 432
- 2020What Can We Learn from Collective Human Opinions on Natural Language Inference Data?cited by 153
- 2020Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESuppositioncited by 77
- 2021OTTers: One-turn Topic Transitions for Open-Domain Dialoguecited by 29
- 2021NOPE: A Corpus of Naturally-Occurring Presuppositions in Englishcited by 23
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