Papers with GS
Injecting Relational Structural Representation in Neural Networks for Question Similarity (P18-2)
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| Challenge: | Recent years have seen exponential growth and use of web forums, where users can exchange and find information just asking questions in natural language. |
| Approach: | They propose to use Tree Kernels to learn a model on relatively few pairs of questions as gold standard (GS) predicting labels on a very large corpus of question pairs is also a useful approach, they propose . |
| Outcome: | The proposed model can learn more accurate models after fine tuning on GS. |
Exploring Logically Dependent Multi-task Learning with Causal Inference (2020.emnlp-main)
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| Challenge: | Hierarchical multi-task learning models can utilize task dependencies by stacking encoders and outperform democratic ones. |
| Approach: | They propose a model that utilizes the labels of all lower-level tasks and a Gumbel sampling model to deal with cascading errors. |
| Outcome: | The proposed model outperforms democratic models on six out of seven subtasks and achieves state-of-the-art on the two English and one Chinese datasets. |
DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)
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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li
| Challenge: | Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage. |
| Approach: | They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation. |
| Outcome: | The proposed model excels on three datasets. |
Select and Reorder: A Novel Approach for Neural Sign Language Production (2024.lrec-main)
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| Challenge: | Sign languages face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. |
| Approach: | They propose a method that breaks down the translation process into two distinct steps: Gloss Selection (GS) and GlosSelection (GR) they use non-autoregressive decoding to achieve faster inference speeds and reduced computation . |
| Outcome: | The proposed method achieves state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated dataset, demonstrating a substantial improvement of 37.88% in Text to Gloss (T2G) Translation. |
Modeling and Solving Stable Matching under Probabilistic Preferences with Large Language Models (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have shown strong capability in understanding and simulating humans’ decisions, suggesting a new way to use LLMs as tools to study social systems. |
| Approach: | They propose a Hybrid GS–LLM matching method that integrates Gale–Shapley with probabilistic acceptance decisions. |
| Outcome: | The proposed method outperforms classical baselines in terms of stability and improves robustness under uncertainty. |