@inproceedings{saadat-yazdi-etal-2022-kevin,
    title = "{KEV}i{N}: A Knowledge Enhanced Validity and Novelty Classifier for Arguments",
    author = {Saadat-Yazdi, Ameer  and
      Li, Xue  and
      Chausson, Sandrine  and
      Belle, Vaishak  and
      Ross, Bj{\"o}rn  and
      Pan, Jeff Z.  and
      K{\"o}kciyan, Nadin},
    booktitle = "Proceedings of the 9th Workshop on Argument Mining",
    month = oct,
    year = "2022",
    address = "Online and in Gyeongju, Republic of Korea",
    publisher = "International Conference on Computational Linguistics",
    url = "https://aclanthology.org/2022.argmining-1.9",
    pages = "104--110",
    abstract = "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.",
}