Papers › Option Comparison Network for Multiple-choice Reading Comprehension

Option Comparison Network for Multiple-choice Reading Comprehension

7 Mar 2019arXiv:1903.03033archive 2025-07-28

Qiu Ran, Peng Li, Weiwei Hu, Jie zhou

Multiple-choice reading comprehension (MCRC) is the task of selecting the correct answer from multiple options given a question and an article. Existing MCRC models typically either read each option independently or compute a fixed-length representation for each option before comparing them. However, humans typically compare the options at multiple-granularity level before reading the article in detail to make reasoning more efficient. Mimicking humans, we propose an option comparison network (OCN) for MCRC which compares options at word-level to better identify their correlations to help reasoning. Specially, each option is encoded into a vector sequence using a skimmer to retain fine-grained information as much as possible. An attention mechanism is leveraged to compare these sequences vector-by-vector to identify more subtle correlations between options, which is potentially valuable for reasoning. Experimental results on the human English exam MCRC dataset RACE show that our model outperforms existing methods significantly. Moreover, it is also the first model that surpasses Amazon Mechanical Turker performance on the whole dataset.

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Tasks

Multiple-choiceQuestion AnsweringReading Comprehension

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering RACE OCN_large RACE 71.7 #2 of 7 Archive leaderboard report
Question Answering RACE OCN_large RACE-h 69.6 #2 of 7 Archive leaderboard report
Question Answering RACE OCN_large RACE-m 76.7 #2 of 7 Archive leaderboard report

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