  Machine-Generated Text (MGT) detection aims to identify a piece of text as
machine or human written. Prior work has primarily formulated MGT detection as
a binary classification task over an entire document, with limited work
exploring cases where only part of a document is machine generated. This paper
provides the first in-depth study of MGT that localizes the portions of a
document that were machine generated. Thus, if a bad actor were to change a key
portion of a news article to spread misinformation, whole document MGT
detection may fail since the vast majority is human written, but our approach
can succeed due to its granular approach. A key challenge in our MGT
localization task is that short spans of text, e.g., a single sentence,
provides little information indicating if it is machine generated due to its
short length. To address this, we leverage contextual information, where we
predict whether multiple sentences are machine or human written at once. This
enables our approach to identify changes in style or content to boost
performance. A gain of 4-13% mean Average Precision (mAP) over prior work
demonstrates the effectiveness of approach on five diverse datasets: GoodNews,
VisualNews, WikiText, Essay, and WP. We release our implementation at
https://github.com/Zhongping-Zhang/MGT_Localization.
