Abstract
The abstract inference capability of the Language Model plays a pivotal role in boosting its generalization and reasoning prowess in Natural Language Inference (NLI). Entailment graphs are crafted precisely for this purpose, focusing on learning entailment relations among predicates. Yet, prevailing approaches overlook the *polysemy* and *hierarchical nature of concepts* during entity conceptualization. This oversight disregards how arguments might entail differently across various concept levels, thereby missing potential entailment connections. To tackle this hurdle, we introduce the *concept pyramid* and propose the HiCon-EG (Hierarchical Conceptual Entailment Graph) framework, which organizes arguments hierarchically, delving into entailment relations at diverse concept levels. By learning entailment relationships at different concept levels, the model is guided to better understand concepts so as to improve its abstract inference capabilities. Our method enhances scalability and efficiency in acquiring common-sense knowledge through leveraging statistical language distribution instead of manual labeling, Experimental results show that entailment relations derived from HiCon-EG significantly bolster abstract detection tasks. Our code is available at https://github.com/SXUCFN/HiCon-EG- Anthology ID:
- 2024.emnlp-main.1233
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 22088–22104
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.emnlp-main.1233/
- DOI:
- 10.18653/v1/2024.emnlp-main.1233
- Cite (ACL):
- Juncai Li, Ru Li, Xiaoli Li, Qinghua Chai, and Jeff Z. Pan. 2024. Inference Helps PLMs’ Conceptual Understanding: Improving the Abstract Inference Ability with Hierarchical Conceptual Entailment Graphs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 22088–22104, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Inference Helps PLMs’ Conceptual Understanding: Improving the Abstract Inference Ability with Hierarchical Conceptual Entailment Graphs (Li et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.emnlp-main.1233.pdf