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arXiv · 1812.02253

Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents

Aditi Chaudhary·Bhargavi Paranjape·Michiel de Jong
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Abstract

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.

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Aditi Chaudhary, Bhargavi Paranjape, Michiel de Jong. 2018-12-05. Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents. https://arxiv.org/abs/1812.02253

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