Improved Machine Reading Comprehension Using Data Validation for Weakly Labeled Data

Abstract

Machine reading comprehension (MRC) is a natural language processing task wherein a given question is answered according to a holistic understanding of a given context. Recently, many researchers have shown interest in MRC, for which a considerable number of datasets are being released. Datasets for MRC, which are composed of the context-query-answer triple, are designed to answer a given query by referencing and understanding a readily-available, relevant context text. The TriviaQA dataset is a weakly labeled dataset, because it contains irrelevant context that forms no basis for answering the query. The existing syntactic data cleaning method struggles to deal with the contextual noise this irrelevancy creates. Therefore, a semantic data cleaning method using reasoning processes is necessary. To address this, we propose a new MRC model in which the TriviaQA dataset is validated and trained using a high-quality dataset. The data validation method in our MRC model improves the quality of the training dataset, and the answer extraction model learns with the validated training data, because of our validation method. Our proposed method showed a 4.33% improvement in performance for the TriviaQA Wiki, compared to the existing baseline model. Accordingly, our proposed method can address the limitation of irrelevant context in MRC better than the human supervision.

Keywords

• Computational and artificial intelligence
• data validation
• natural language processing
• neural networks
• machine reading comprehension
• weak label