Design of A Recurrent Neural Network Model for Machine Reading Comprehension

被引：5

作者：

Singh, Uttam ^{[1
]}

Kedas, Shweta ^{[1
]}

Prasanth, Sikakollu ^{[1
]}

Kumar, Arun ^{[1
]}

Semwal, Vijay Bhaskar ^{[2
]}

Tikkiwal, Vinay Anand ^{[3
]}

机构：

[1] NIT, Dept Comp Sci & Engn, Rourkela 769008, India

[2] MANIT Bhopal, Dept Comp Sci & Engn, Bhopal 462003, India

[3] Jaypee Inst Informat Technol, Noida 201304, India

来源：

INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE | 2020年 / 167卷

关键词：

Stanford Question and Answer data set (SQuAD); Machine Reading Comprehension (MRC); Machine Learning; Recurrent Neural Network (RNN); Gated Recurrent Unit (GRU); Global Vectors for word representation (GloVe);

D O I：

10.1016/j.procs.2020.03.388

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Reading paragraphs, understanding the related questions and answering them has always been a difficult task for the machines. Humans have the capability to understand the logic and meaning of a question and answer it to maintain a proper mode of interaction. But for machines, this is a complex task. The main focus of this research work is to explore the various machine learning and neural networks based techniques to develop and train a model on context of paragraphs related questions and answers and then test the model on an user given paragraph and question. This paper presents a thorough understanding of data analysis of the SQuAD data set and word embedding applied on the questions and answers of training set of the SQuAD data set. (C) 2020 The Authors. Published by Elsevier B.V.

引用

页码：1791 / 1800

页数：10

共 13 条

[1] [Anonymous], 2017, R NET MACH READ COMP
[2] Baalbaki Wissam, 2018, CS224N NATURAL LANGU
[3] Bahdanau D., 2014, P INT C LEARN REPR
[4] Cho K, 2014, PREPRINT
[5] Jiang Hui, 2018, ARXIV180903449
[6] Mikolov T., 2013, ADV NEURAL INFORM PR, P3111, DOI DOI 10.5555/2999792.2999959
[7] Pennington J, 2014, P 2014 C EMP METH NA, P1532
[8] Rajpurkar Pranav, 2016, ARXIV160605250V3
[9] Bidirectional recurrent neural networks
Schuster, M
Paliwal, KK
[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1997, 45 (11) : 2673 - 2681
[10] Srivastava N, 2014, J MACH LEARN RES, V15, P1929

← 1 2 →