# A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement

arXiv: https://arxiv.org/abs/2404.04035 · HTML: https://mlsanity.com/paper/2404.04035/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement](https://mlsanity.com/paper/2109.13563/) (2021)
- [Stop Measuring Calibration When Humans Disagree](https://mlsanity.com/paper/2210.16133/) (2022)
- [Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia](https://mlsanity.com/paper/2106.01601/) (2021)
- [Modeling Semantic Plausibility by Injecting World Knowledge](https://mlsanity.com/paper/1804.00619/) (2018)
- [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://mlsanity.com/paper/2104.10247/) (2021)
- [Learning Supervised Topic Models for Classification and Regression from Crowds](https://mlsanity.com/paper/1808.05902/) (2018)
- [Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge](https://mlsanity.com/paper/2107.10922/) (2021)
- [The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing](https://mlsanity.com/paper/2106.08037/) (2021)
- [Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text](https://mlsanity.com/paper/1911.05689/) (2019)
- [SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition](https://mlsanity.com/paper/1906.02123/) (2019)

## Built on by (most cited first, 3 of 3)

- [Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge](https://mlsanity.com/paper/2408.16937/) (2024)
- [Finding Sense in Nonsense with Generated Contexts: Perspectives from Humans and Language Models](https://mlsanity.com/paper/2602.11699/) (2026)
- [Contextualising (Im)plausible Events Triggers Figurative Language](https://mlsanity.com/paper/2604.07885/) (2026)

## Abstract (arXiv)

We present a novel dataset for physical and abstract plausibility of events in English. Based on naturally occurring sentences extracted from Wikipedia, we infiltrate degrees of abstractness, and automatically generate perturbed pseudo-implausible events. We annotate a filtered and balanced subset for plausibility using crowd-sourcing, and perform extensive cleansing to ensure annotation quality. In-depth quantitative analyses indicate that annotators favor plausibility over implausibility and disagree more on implausible events. Furthermore, our plausibility dataset is the first to capture abstractness in events to the same extent as concreteness, and we find that event abstractness has an impact on plausibility ratings: more concrete event participants trigger a perception of implausibility.