@inproceedings{varun-etal-2022-trans,
    title = "Trans-{KBLSTM}: An External Knowledge Enhanced Transformer {B}i{LSTM} Model for Tabular Reasoning",
    author = "Varun, Yerram  and
      Sharma, Aayush  and
      Gupta, Vivek",
    editor = "Agirre, Eneko  and
      Apidianaki, Marianna  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = may,
    year = "2022",
    address = "Dublin, Ireland and Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.deelio-1.7/",
    doi = "10.18653/v1/2022.deelio-1.7",
    pages = "62--78",
    abstract = "Natural language inference on tabular data is a challenging task. Existing approaches lack the world and common sense knowledge required to perform at a human level. While massive amounts of KG data exist, approaches to integrate them with deep learning models to enhance tabular reasoning are uncommon. In this paper, we investigate a new approach using BiLSTMs to incorporate knowledge effectively into language models. Through extensive analysis, we show that our proposed architecture, Trans-KBLSTM improves the benchmark performance on InfoTabS, a tabular NLI dataset."
}