# Event Representation Learning Enhanced with External Commonsense Knowledge

arXiv: https://arxiv.org/abs/1909.05190 · HTML: https://mlsanity.com/paper/1909.05190/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [EventBERT: A Pre-Trained Model for Event Correlation Reasoning](https://mlsanity.com/paper/2110.06533/) (2021)
- [DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge](https://mlsanity.com/paper/2101.00154/) (2021)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Integrating Deep Event-Level and Script-Level Information for Script Event Prediction](https://mlsanity.com/paper/2110.15706/) (2021)
- [Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://mlsanity.com/paper/2203.07633/) (2022)
- [SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller](https://mlsanity.com/paper/2210.06694/) (2022)
- [A Generative Approach for Script Event Prediction via Contrastive Fine-tuning](https://mlsanity.com/paper/2212.03496/) (2022)
- [EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models](https://mlsanity.com/paper/2305.15268/) (2023)
- [A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems](https://mlsanity.com/paper/2106.05830/) (2021)
- [The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation](https://mlsanity.com/paper/2503.03308/) (2025)
- [Probing Script Knowledge from Pre-Trained Models](https://mlsanity.com/paper/2204.10176/) (2022)
- [Rich Event Modeling for Script Event Prediction](https://mlsanity.com/paper/2212.08287/) (2022)
- [Enhancing Few-Shot Stock Trend Prediction with Large Language Models](https://mlsanity.com/paper/2407.09003/) (2024)
- [DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies](https://mlsanity.com/paper/2203.08144/) (2022)
- [Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction](https://mlsanity.com/paper/2308.02103/) (2023)
- [PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning](https://mlsanity.com/paper/2404.17877/) (2024)
- [Abstract Event Causal Rules: Induction and Application](https://mlsanity.com/paper/2608.05205/) (2026)

## Abstract (arXiv)

Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market.