Papers › Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains
Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains
Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, D. Song, an, Lejian Liao
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentence Compression | Google Dataset | BiLSTM | CR | 0.43 | #6 of 6 | Archive leaderboard | report |
| Sentence Compression | Google Dataset | BiLSTM | F1 | 0.8 | #6 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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