Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning
Yerram Varun, Aayush Sharma, Vivek Gupta · 2022
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.