일반 상식 기반 기계 독해를 위한 Type-specific 다중 헤드 공유 인코더 모델

초록

Machine reading comprehension (MRC) is a task introduced to a machine that can understand natural languages by solving various tasks based on given context. To evaluate natural language understanding of machine, a machine must make commonsense inference under full comprehension of a given context. To enhance model obtaining such abilities, we proposed a multi-task learning scheme and a model for commonsense MRC. Contributions of this study are as follows: 1) a method of task-specific dataset configuration is proposed; 2) a type-specific multi-head shared-encoder model with multi-task learning scheme including batch sampling and loss scaling is developed; and 3) when the method is evaluated on CosmosQA dataset (commonsense MRC), the accuracy was improved by 2.38% compared to the performance at baseline with fine-tuning.

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