@inproceedings{01fd7f67a90c4ac7953cbc2c8baf7887,
title = "Can syntax help? Improving an LSTM-based sentence compression model for new domains",
abstract = "In this paper, we study how to improve the domain adaptability of a deletion-based Long Short-Term Memory (LSTM) neural network model for sentence compression. We hypothesize that syntactic information helps in making such models more robust across domains. We propose two major changes to the model: using explicit syntactic features and introducing syntactic constraints through Integer Linear Programming (ILP). Our evaluation shows that the proposed model works better than the original model as well as a traditional non-neural-network-based model in a cross-domain setting.",
author = "Liangguo Wang and Jing Jiang and Chieu, \{Hai Leong\} and Ong, \{Chen Hui\} and Dandan Song and Lejian Liao",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics.; 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 ; Conference date: 30-07-2017 Through 04-08-2017",
year = "2017",
doi = "10.18653/v1/P17-1127",
language = "English",
series = "ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1385--1393",
booktitle = "ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",
address = "United States",
}