Frame-based Neural Network for Machine Reading Comprehension

Machine Reading Comprehension (MRC) is one of the most challenging tasks in Natural Language Understanding (NLU). In particular, MRC systems typically answer a question by only utilizing the information contained in a given piece of text passage itself, while human beings can easily understand the meanings of the passage based on their background knowledge. To bridge the gap, we propose a novel Frame-based Neural Network for Machine Reading Comprehension(FNN-MRC) method, which employs Frame semantic knowledge to facilitate question answering. Specifically, different from existing Frame based methods that only model lexical units (LUs), our FNN-MRC has a Frame representation model, which utilizes both LUs in Frame and Frame-to-Frame (F-to-F) relations, designed to model Frames and sentences (in passage) together with attention schema. In addition, FNN-MRC has a Frame-based Sentence Representation (FSR) model, which is able to integrate multiple-Frame semantic information to obtain much better sentence representation. As such, FNN-MRC explicitly leverages the above Frame knowledge to assist its semantic understanding and representation. Extensive experiments demonstrate that our FNN-MRC method is able to achieve better results than existing state-of-the-art techniques across multiple datasets.