Smarnet: Teaching Machines to Read and Comprehend Like Human

By Zheqian Chen, Rongqin Yang, Bin Cao, Zhou Zhao, Deng Cai, Xiaofei He. 2017

23 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Simple and Effective Multi-Paragraph Reading Comprehension (2017, methodology): “Open question answering with neural models was considered by Chen et al. (2017), where researchers trained a model on SQuAD and combined it with a retrieval engine for Wikipedia articles.”
The Effect of Natural Distribution Shift on Question Answering Models (2020, neutral): “Smarnet (Ensemble) (Chen et al., 2017) https://worksheets.”
Making Neural Machine Reading Comprehension Faster (2019, methodology): “ationally faster neural architecture which performs reasonably well on SQuAD task. 2 Chapter 2 Literature Review Most of the papers that approach to solve SQuAD problem have utilized RNN based models [3, 5, 7, 9, 13, 14, 16, 17, 20, 22{24, 26, 29{31, 34{39, 41, 43] and they follow a similar chain of”