Papers › Long Short-Term Memory-Networks for Machine Reading
Long Short-Term Memory-Networks for Machine Reading
Jianpeng Cheng, Li Dong, Mirella Lapata
In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a machine reading simulator which processes text incrementally from left to right and performs shallow reasoning with memory and attention. The reader extends the Long Short-Term Memory architecture with a memory network in place of a single memory cell. This enables adaptive memory usage during recurrence with neural attention, offering a way to weakly induce relations among tokens. The system is initially designed to process a single sequence but we also demonstrate how to integrate it with an encoder-decoder architecture. Experiments on language modeling, sentiment analysis, and natural language inference show that our model matches or outperforms the state of the art.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | 450D LSTMN with deep attention fusion | % Test Accuracy | 86.3 | #60 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 450D LSTMN with deep attention fusion | % Train Accuracy | 88.5 | #60 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 450D LSTMN with deep attention fusion | Parameters | 3.4m | #60 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D LSTMN with deep attention fusion | % Test Accuracy | 85.7 | #68 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D LSTMN with deep attention fusion | % Train Accuracy | 87.3 | #68 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 300D LSTMN with deep attention fusion | Parameters | 1.7m | #68 of 98 | 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.
Methods
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