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.

Xuefeng Su, Ru Li, Xiaoli Li, Jeff Z. Pan, Hu Zhang, Qinghua Chai, Xiaoqi Han. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Comprehending unstructured text is a challenging task for machines because it involves understanding texts and answering questions. In this paper, we study the multiple‐choice task for reading comprehension based on MC Test datasets and Chinese reading comprehension datasets, among which Chinese reading comprehension datasets which are built by ourselves. Observing the above‐mentioned training sets, we find that “sentence comprehension” is more important than “word comprehension” in multiple‐choice task, and therefore we propose sentence‐level neural network models. Our model firstly uses LSTM network and a composition model to learn compositional vector representation for sentences and then trains a sentence‐level attention model for obtaining the sentence‐level attention between the sentence embedding in documents and the optional sentences embedding by dot product. Finally, a consensus attention is gained by merging individual attention with the merging function. Experimental results show that our model outperforms various state‐of‐the‐art baselines significantly for both the multiple‐choice reading comprehension datasets.

Ru Li, Juan Wu, Zhiqiang Wang, Qinghua Chai. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.