# Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text

arXiv: https://arxiv.org/abs/1911.05689 · HTML: https://mlsanity.com/paper/1911.05689/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://mlsanity.com/paper/2104.10247/) (2021)
- [Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge](https://mlsanity.com/paper/2107.10922/) (2021)
- [A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement](https://mlsanity.com/paper/2404.04035/) (2024)
- [Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain](https://mlsanity.com/paper/2304.14745/) (2023)
- [Understanding in Artificial Intelligence](https://mlsanity.com/paper/2101.06573/) (2021)
- [Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities](https://mlsanity.com/paper/2210.17440/) (2022)
- [Plausibility Vaccine: Injecting LLM Knowledge for Event Plausibility](https://mlsanity.com/paper/2503.12667/) (2025)
- [Modelling Adjectival Modification Effects on Semantic Plausibility](https://mlsanity.com/paper/2507.21828/) (2025)
- [Contextualising (Im)plausible Events Triggers Figurative Language](https://mlsanity.com/paper/2604.07885/) (2026)

## Abstract (arXiv)

Modeling semantic plausibility requires commonsense knowledge about the world and has been used as a testbed for exploring various knowledge representations. Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervised setting. At the same time, distributional models, namely large pretrained language models, have led to improved results for many natural language understanding tasks. In this work, we show that these pretrained language models are in fact effective at modeling physical plausibility in the supervised setting. We therefore present the more difficult problem of learning to model physical plausibility directly from text. We create a training set by extracting attested events from a large corpus, and we provide a baseline for training on these attested events in a self-supervised manner and testing on a physical plausibility task. We believe results could be further improved by injecting explicit commonsense knowledge into a distributional model.