Lejian Liao


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Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction
Ge Shi | Chong Feng | Lifu Huang | Boliang Zhang | Heng Ji | Lejian Liao | Heyan Huang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1% absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7% absolute F1 gain. We make all programs of our model publicly available for research purpose


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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.


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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