Procedural text understanding is a challenging language reasoning task that
requires models to track entity states across the development of a narrative. A
complete procedural understanding solution should combine three core aspects:
local and global views of the inputs, and global view of outputs. Prior methods
considered a subset of these aspects, resulting in either low precision or low
recall. In this paper, we propose Coalescing Global and Local Information
(CGLI), a new model that builds entity- and timestep-aware input
representations (local input) considering the whole context (global input), and
we jointly model the entity states with a structured prediction objective
(global output). Thus, CGLI simultaneously optimizes for both precision and
recall. We extend CGLI with additional output layers and integrate it into a
story reasoning framework. Extensive experiments on a popular procedural text
understanding dataset show that our model achieves state-of-the-art results;
experiments on a story reasoning benchmark show the positive impact of our
model on downstream reasoning.

本文提出了一种新的模型 CGLI，该模型结合了本地和全局视图的输入以及全局输出，旨在实现精度和召回率同时优化，同时在故事推理框架中进行了扩展，实验结果表明这种方法在流程文本理解和故事推理方面均取得了最先进的结果。