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The current trend in developing machine learning models for readingcomprehension and logical reasoning tasks is focused on improving the models'abilities to understand and utilize logical rules. This work focuses onproviding a novel loss function and accompanying model architecture that hasmore interpretable components than some other models by representing a commonstrategy employed by humans when given reading comprehension and logicalreasoning tasks. Our strategy involves emphasizing relative accuracy overabsolute accuracy and can theoretically produce the correct answer withincomplete knowledge. We examine the effectiveness of this strategy to solvereading comprehension and logical reasoning questions. The models wereevaluated on the ReClor dataset, a challenging reading comprehension andlogical reasoning benchmark. We propose the polytuplet loss function, whichforces prioritization of learning the relative correctness of answer choicesover learning the true accuracy of each choice. Our results indicate thatmodels employing polytuplet loss outperform existing baseline models, thoughfurther research is required to quantify the benefits it may present.

Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss