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Machine reading comprehension(MRC) has attracted significant amounts ofresearch attention recently, due to an increase of challenging readingcomprehension datasets. In this paper, we aim to improve a MRC model's abilityto determine whether a question has an answer in a given context (e.g. therecently proposed SQuAD 2.0 task). Our solution is a relation module that isadaptable to any MRC model. The relation module consists of both semanticextraction and relational information. We first extract high level semantics asobjects from both question and context with multi-head self-attentive pooling.These semantic objects are then passed to a relation network, which generatesrelationship scores for each object pair in a sentence. These scores are usedto determine whether a question is non-answerable. We test the relation moduleon the SQuAD 2.0 dataset using both BiDAF and BERT models as baseline readers.We obtain 1.8% gain of F1 on top of the BiDAF reader, and 1.0% on top of theBERT base model. These results show the effectiveness of our relation module onMRC

Relation Module for Non-answerable Prediction on Question Answering