# Modeling Event Plausibility with Consistent Conceptual Abstraction

arXiv: https://arxiv.org/abs/2104.10247 · HTML: https://mlsanity.com/paper/2104.10247/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 22 of 22)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [Hypothesis Only Baselines in Natural Language Inference](https://mlsanity.com/paper/1805.01042/) (2018)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [Universal Dependency Parsing from Scratch](https://mlsanity.com/paper/1901.10457/) (2019)
- [Can neural networks understand monotonicity reasoning?](https://mlsanity.com/paper/1906.06448/) (2019)
- [Probing Linguistic Systematicity](https://mlsanity.com/paper/2005.04315/) (2020)
- [Ordinal Common-sense Inference](https://mlsanity.com/paper/1611.00601/) (2016)
- [A Surprisingly Robust Trick for Winograd Schema Challenge](https://mlsanity.com/paper/1905.06290/) (2019)
- [Modeling Semantic Plausibility by Injecting World Knowledge](https://mlsanity.com/paper/1804.00619/) (2018)
- [Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences](https://mlsanity.com/paper/2009.11795/) (2020)
- [Knowledge-aware Pronoun Coreference Resolution](https://mlsanity.com/paper/1907.03663/) (2019)
- [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)
- [On the Role of Conceptualization in Commonsense Knowledge Graph Construction](https://mlsanity.com/paper/2003.03239/) (2020)
- [Representing Verbs as Argument Concepts](https://mlsanity.com/paper/1803.00729/) (2018)
- [Ontology-Aware Token Embeddings for Prepositional Phrase Attachment](https://mlsanity.com/paper/1705.02925/) (2017)

## Built on by (most cited first, 20 of 20)

- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Event knowledge in large language models: the gap between the impossible and the unlikely](https://mlsanity.com/paper/2212.01488/) (2022)
- [Editing Common Sense in Transformers](https://mlsanity.com/paper/2305.14956/) (2023)
- [A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2106.02097/) (2021)
- [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://mlsanity.com/paper/2305.04808/) (2023)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Commonsense Knowledge Editing Based on Free-Text in LLMs](https://mlsanity.com/paper/2410.23844/) (2024)
- [Reasons and Solutions for the Decline in Model Performance after Editing](https://mlsanity.com/paper/2410.23843/) (2024)
- [Better Language Model with Hypernym Class Prediction](https://mlsanity.com/paper/2203.10692/) (2022)
- [Does Entity Abstraction Help Generative Transformers Reason?](https://mlsanity.com/paper/2201.01787/) (2022)
- [Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios](https://mlsanity.com/paper/2209.07760/) (2022)
- [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)
- [Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models](https://mlsanity.com/paper/2506.08427/) (2025)
- [EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion](https://mlsanity.com/paper/2402.09666/) (2024)
- [Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge](https://mlsanity.com/paper/2408.16937/) (2024)
- [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)
- [Break Through the Compression Bottleneck: From Theory to Practice](https://mlsanity.com/paper/2607.20434/) (2026)

## Abstract (arXiv)

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models -- most recently pre-trained, Transformer language models -- have demonstrated improvements in modeling event plausibility, their performance still falls short of humans'. In this work, we show that Transformer-based plausibility models are markedly inconsistent across the conceptual classes of a lexical hierarchy, inferring that "a person breathing" is plausible while "a dentist breathing" is not, for example. We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements.