  We introduce a novel Dual Input Stream Transformer (DIST) for the challenging
problem of assigning fixation points from eye-tracking data collected during
passage reading to the line of text that the reader was actually focused on.
This post-processing step is crucial for analysis of the reading data due to
the presence of noise in the form of vertical drift. We evaluate DIST against
nine classical approaches on a comprehensive suite of nine diverse datasets,
and demonstrate DIST's superiority. By combining multiple instances of the DIST
model in an ensemble we achieve an average accuracy of 98.5\% across all
datasets. Our approach presents a significant step towards addressing the
bottleneck of manual line assignment in reading research. Through extensive
model analysis and ablation studies, we identify key factors that contribute to
DIST's success, including the incorporation of line overlap features and the
use of a second input stream. Through evaluation on a set of diverse datasets
we demonstrate that DIST is robust to various experimental setups, making it a
safe first choice for practitioners in the field.

Computer Vision and Pattern Recognition;Machine Learning