# Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder

arXiv: https://arxiv.org/abs/1909.08824 · HTML: https://mlsanity.com/paper/1909.08824/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [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)
- [Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance](https://mlsanity.com/paper/2403.09085/) (2024)
- [COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities](https://mlsanity.com/paper/2209.06470/) (2022)

## Abstract (arXiv)

Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP). Given an observed event, it is trivial for human to infer its intents and effects, while this type of If-Then reasoning still remains challenging for NLP systems. To facilitate this, a If-Then commonsense reasoning dataset Atomic is proposed, together with an RNN-based Seq2Seq model to conduct such reasoning. However, two fundamental problems still need to be addressed: first, the intents of an event may be multiple, while the generations of RNN-based Seq2Seq models are always semantically close; second, external knowledge of the event background may be necessary for understanding events and conducting the If-Then reasoning. To address these issues, we propose a novel context-aware variational autoencoder effectively learning event background information to guide the If-Then reasoning. Experimental results show that our approach improves the accuracy and diversity of inferences compared with state-of-the-art baseline methods.