Publication Type

Conference Proceeding Article

Version

Publisher’s Version

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://openreview.net/forum?id=HJTzHtqee

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