KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments
Ameer Saadat-Yazdi | Xue Li | Sandrine Chausson | Vaishak Belle | Björn Ross | Jeff Z. Pan | Nadin Kökciyan
Proceedings of the 9th Workshop on Argument Mining
The ArgMining 2022 Shared Task is concerned with predicting the validity and novelty of an inference for a given premise and conclusion pair. We propose two feed-forward network based models (KEViN1 and KEViN2), which combine features generated from several pretrained transformers and the WikiData knowledge graph. The transformers are used to predict entailment and semantic similarity, while WikiData is used to provide a semantic measure between concepts in the premise-conclusion pair. Our proposed models show significant improvement over RoBERTa, with KEViN1 outperforming KEViN2 and obtaining second rank on both subtasks (A and B) of the ArgMining 2022 Shared Task.
Learning Causal Bayesian Networks from Text
Farhad Moghimifar | Afshin Rahimi | Mahsa Baktashmotlagh | Xue Li
Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association
Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.