  Our goal is procedural text comprehension, namely tracking how the properties
of entities (e.g., their location) change with time given a procedural text
(e.g., a paragraph about photosynthesis, a recipe). This task is challenging as
the world is changing throughout the text, and despite recent advances, current
systems still struggle with this task. Our approach is to leverage the fact
that, for many procedural texts, multiple independent descriptions are readily
available, and that predictions from them should be consistent (label
consistency). We present a new learning framework that leverages label
consistency during training, allowing consistency bias to be built into the
model. Evaluation on a standard benchmark dataset for procedural text, ProPara
(Dalvi et al., 2018), shows that our approach significantly improves prediction
performance (F1) over prior state-of-the-art systems.
