A compare-aggregate model for matching text sequences

• Main Authors: WANG, Shuohang, Jing JIANG
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Natural language processing; Deep learning; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/3653; https://ink.library.smu.edu.sg/context/sis_research/article/4655/viewcontent/14._Apr03_2017___A_Compare__Aggregate_Model_For_Matching_Text_Sequences__ICLR2017_.pdf
• Institution: Singapore Management University

Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general "compare-aggregate" framework that performs wo...