  U.S. Federal Regulators receive over one million comment letters each year
from businesses, interest groups, and members of the public, all advocating for
changes to proposed regulations. These comments are believed to have
wide-ranging impacts on public policy. However, measuring the impact of
specific comments is challenging because regulators are required to respond to
comments but they do not have to specify which comments they are addressing. In
this paper, we propose a simple yet effective solution to this problem by using
an iterative contrastive method to train a neural model aiming for matching
text from public comments to responses written by regulators. We demonstrate
that our proposal substantially outperforms a set of selected text-matching
baselines on a human-annotated test set. Furthermore, it delivers performance
comparable to the most advanced gigantic language model (i.e., GPT-4), and is
more cost-effective when handling comments and regulator responses matching in
larger scale.
