Abstract
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.- Anthology ID:
- D19-1270
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2682–2691
- Language:
- URL:
- https://aclanthology.org/D19-1270
- DOI:
- 10.18653/v1/D19-1270
- Cite (ACL):
- Li Du, Xiao Ding, Ting Liu, and Zhongyang Li. 2019. Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2682–2691, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder (Du et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D19-1270.pdf
- Data
- Event2Mind, ROCStories, VIST, WritingPrompts