  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
eleven classical approaches on a comprehensive suite of nine diverse datasets.
We demonstrate that combining multiple instances of the DIST model in an
ensemble achieves high accuracy across all datasets. Further combining the DIST
ensemble with the best classical approach yields an average accuracy of 98.17
%. Our approach presents a significant step towards addressing the bottleneck
of manual line assignment in reading research. Through extensive 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. Via rigorous evaluation, we demonstrate that DIST is robust to
various experimental setups, making it a safe first choice for practitioners in
the field.
