Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2017
Abstract
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.
Keywords
Natural language processing, Deep learning
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
ICLR 2017: International Conference on Learning Representations, Toulon, France, April 24-26: Proceedings
First Page
1
Last Page
15
Publisher
ICLR
City or Country
Amherst, MA
Citation
WANG, Shuohang and Jing JIANG.
A compare-aggregate model for matching text sequences. (2017). ICLR 2017: International Conference on Learning Representations, Toulon, France, April 24-26: Proceedings. 1-15.
Available at: https://ink.library.smu.edu.sg/sis_research/3653
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://openreview.net/forum?id=HJTzHtqee