Papers with CLSC
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)
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| Challenge: | Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. |
| Approach: | They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet. |
| Outcome: | The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin. |
Cross-Lingual Sentence Compression for Length-Constrained Subtitles in Low-Resource Settings (2025.coling-main)
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| Challenge: | a new system for sentence compression is developed for broadcast and live media . the goal is to reduce the on-screen visual constraints of the text . |
| Approach: | They develop a machine translation and sentence compression system that trains on openly available parallel corpora organized by compression ratios. |
| Outcome: | The proposed system preserves high semantic meaning and metric evaluations for compressed contexts. |