Motivated by recent evidence pointing out the fragility of high-performing span prediction models, we direct our attention to multiple choice reading comprehension. In particular, this work introduces a novel method for improving answer selection on long documents through weighted global normalization of predictions over portions of the documents. We show that applying our method to a span prediction model adapted for answer selection helps model performance on long summaries from NarrativeQA, a challenging reading comprehension dataset with an answer selection task, and we strongly improve on the task baseline performance by +36.2 Mean Reciprocal Rank.
@article{arxiv.1812.02253,
title = {Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents},
author = {Aditi Chaudhary and Bhargavi Paranjape and Michiel de Jong},
journal= {arXiv preprint arXiv:1812.02253},
year = {2021}
}