Question Directed Graph Attention Network for Numerical Reasoning over Text

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP 1 . * Corresponding author 1 https://leaderboard.allenai.org/drop/submissions/public . As of September 08, 2020, our models are ranked first in the case of fair comparison using the identical pre-training model.