# Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge

arXiv: https://arxiv.org/abs/2408.16937 · HTML: https://mlsanity.com/paper/2408.16937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 29 Aug 2024.

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

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model](https://mlsanity.com/paper/2106.04098/) (2021)
- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [Verb Physics: Relative Physical Knowledge of Actions and Objects](https://mlsanity.com/paper/1706.03799/) (2017)
- [Modeling Semantic Plausibility by Injecting World Knowledge](https://mlsanity.com/paper/1804.00619/) (2018)
- [Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects](https://mlsanity.com/paper/1602.00753/) (2016)
- [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://mlsanity.com/paper/2104.10247/) (2021)
- [GLEN: General-Purpose Event Detection for Thousands of Types](https://mlsanity.com/paper/2303.09093/) (2023)
- [Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing](https://mlsanity.com/paper/2311.00835/) (2023)
- [A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement](https://mlsanity.com/paper/2404.04035/) (2024)

## Abstract (arXiv)

In this work, we investigate the effectiveness of injecting external knowledge to a large language model (LLM) to identify semantic plausibility of simple events. Specifically, we enhance the LLM with fine-grained entity types, event types and their definitions extracted from an external knowledge base. These knowledge are injected into our system via designed templates. We also augment the data to balance the label distribution and adapt the task setting to real world scenarios in which event mentions are expressed as natural language sentences. The experimental results show the effectiveness of the injected knowledge on modeling semantic plausibility of events. An error analysis further emphasizes the importance of identifying non-trivial entity and event types.