Papers with data-driven method
Controllable Sentence Simplification in Swedish Using Control Prefixes and Mined Paraphrases (2024.lrec-main)
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| Challenge: | Automated Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. |
| Approach: | They propose to use a dataset of Swedish paraphrases to train ATS models utilizing prefix-tuning with control prefixes to provide more control over the simplification. |
| Outcome: | The proposed model improves on the baseline model and compares with previous models. |
A Data-Driven Method for Analyzing and Quantifying Lyrics-Dance Motion Relationships (2025.naacl-long)
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| Challenge: | Existing studies have not explored the relationships between lyrics and dance motions . previous studies focused on synthesizing or retrieving dance motion from lyrics . |
| Approach: | They propose a method to detect parts of songs where meaningful relationships exist . they use clustering to transform lyrics and dance motions into symbols . |
| Outcome: | The proposed method outperforms existing methods on prose and non-dance dance motions. |
Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)
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| Challenge: | Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy. |
| Approach: | They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata. |
| Outcome: | The proposed framework outperforms state-of-the-art models by approximately 40% in edge accuracy on datasets like Asia and Sachs while maintaining strong performance on more complex graphs. |
How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models? (2022.lrec-1)
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| Challenge: | Recent studies have shown that neural encoding models explore brain language processing using naturalistic stimuli. |
| Approach: | They propose a block-wise cross-validation training method and an adequate data size for increasing the performance of neural encoding models. |
| Outcome: | The proposed training method and data size can significantly decrease the performance of neural encoding models in the temporal and frontal lobes. |