Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2018

Abstract

Deletion-based sentence compression is frequently formulated as a constrained optimization problem and solved by integer linear programming (ILP). However, ILP methods searching the best compression given the space of all possible compressions would be intractable when dealing with overly long sentences and too many constraints. Moreover, the hard constraints of ILP would restrict the available solutions. This problem could be even more severe considering parsing errors. As an alternative solution, we formulate this task in a reinforcement learning framework, where hard constraints are used as rewards in a soft manner. The experiment results show that our method achieves competitive performance with a large improvement on the speed.

Keywords

Sentence compression, Deep reinforcement learning

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 11th International Conference, KSEM 2018, Changchun, China, August 17-19

Volume

11061 LNAI

First Page

3

Last Page

15

Identifier

10.1007/978-3-319-99365-2_1

Publisher

Springer

City or Country

Cham

Comments

Cited by: 3

Additional URL

https://doi.org/10.1007/978-3-319-99365-2_1

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