2021/04/09 by J. Neeraja, Vivek Gupta, Neeraja, J. +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #cs.AI #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.2104.04243
11 pages, 1 Figure, 14 tables, To appear in NAACL 2021 (Short paper)
arxiv created 2021/04/09 · arxiv updated 2021/04/12
Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text. In this paper, we study these challenges through the problem of tabular natural language inference. We propose easy and effective modifications to how information is presented to a model for this task. We show via systematic experiments that these strategies substantially improve tabular inference performance.