Dandan Song


2017

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Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains
Liangguo Wang | Jing Jiang | Hai Leong Chieu | Chen Hui Ong | Dandan Song | Lejian Liao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.

2015

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LDTM: A Latent Document Type Model for Cumulative Citation Recommendation
Jingang Wang | Dandan Song | Zhiwei Zhang | Lejian Liao | Luo Si | Chin-Yew Lin
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing