A compare-aggregate model for matching text sequences

A compare-aggregate model for matching text sequences

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...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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 word-level matching followed by aggregation using Convolutional Neural Networks. We particularly focus on the different comparison functions we can use to match two vectors. We use four different datasets to evaluate the model. We find that some simple comparison functions based on element-wise operations can work better than standard neural network and neural tensor network.

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