Smarnet: Teaching Machines to Read and Comprehend Like Human

Abstract

Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question. Many existing approaches on MC task are suffering the inefficiency in some bottlenecks, such as insufficient lexical understanding, complex question-passage interaction, incorrect answer extraction and so on. In this paper, we address these problems from the viewpoint of how humans deal with reading tests in a scientific way. Specifically, we first propose a novel lexical gating mechanism to dynamically combine the words and characters representations. We then guide the machines to read in an interactive way with attention mechanism and memory network. Finally we add a checking layer to refine the answer for insurance. The extensive experiments on two popular datasets SQuAD and TriviaQA show that our method exceeds considerable performance than most state-of-the-art solutions at the time of submission.

8 pages, paper for SQuAD machine comprehension

References in corpus (4)

• ADADELTA: An Adaptive Learning Rate Method
• NLTK: The Natural Language Toolkit
• Machine Comprehension Using Match-LSTM and Answer Pointer
• MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension

Cited by in corpus (5)

• The Effect of Natural Distribution Shift on Question Answering Models
• Making Neural Machine Reading Comprehension Faster
• Integrated Triaging for Fast Reading Comprehension
• Deep Understanding based Multi-Document Machine Reading Comprehension
• An Understanding-Oriented Robust Machine Reading Comprehension Model