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This paper analyzes challenges in cloze-style reading comprehension onmultiparty dialogue and suggests two new tasks for more comprehensivepredictions of personal entities in daily conversations. We first demonstratethat there are substantial limitations to the evaluation methods of previouswork, namely that randomized assignment of samples to training and test datasubstantially decreases the complexity of cloze-style reading comprehension.According to our analysis, replacing the random data split with a chronologicaldata split reduces test accuracy on previous single-variable passage completiontask from 72\% to 34\%, that leaves much more room to improve. Our proposedtasks extend the previous single-variable passage completion task by replacingmore character mentions with variables. Several deep learning models aredeveloped to validate these three tasks. A thorough error analysis is providedto understand the challenges and guide the future direction of this research.

Design and Challenges of Cloze-Style Reading Comprehension Tasks on Multiparty Dialogue