Summary of Hierarchical Knowledge Distillation on Text Graph For Data-limited Attribute Inference, by Quan Li et al.
Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference
by Quan Li, Shixiong Jing, Lingwei Chen
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed few-shot learning model for attribute inference on social media text data leverages a text-graph-based approach to improve expressiveness and complexity, while also utilizing cross-domain texts and unlabeled texts to enhance generalization ability. By refining a text graph using manifold learning and message passing, the model offers a better trade-off between expressiveness and complexity. Additionally, hierarchical knowledge distillation is employed to optimize the problem and derive better text representations, leading to state-of-the-art performance on attribute inferences with fewer labeled texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand people better by analyzing their social media posts. It’s like trying to figure out someone’s age or where they’re from just by reading what they write online! The researchers use special computer models that look at the words and connections between them to make predictions about who someone is and what they might be interested in. They found a way to improve these models so they can learn more with less information, which makes it easier for computers to understand us. |
Keywords
* Artificial intelligence * Few shot * Generalization * Inference * Knowledge distillation * Manifold learning
