Papers with Poincaré embeddings
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings (P19-1)
Copied to clipboard
| Challenge: | idiomatic phrases have a non-compositional meaning, meanings of which can be derived from constituents and their grammatical relations. |
| Approach: | They propose to combine hierarchical and distributional information to blend hierarchic and distribution-based hierarchies to detect compositionality for noun phrases. |
| Outcome: | The proposed technique achieves significant improvements over state-of-the-art models based on distributional information and a weighted average of the distributional similarity and p-like function. |
Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to domain-specific taxonomy induction from text are relying on distributional semantics for hyponym-hypernym relationships, but many of them learn prototypical hypernymes, not taking into account the relation between both terms in classification. |
| Approach: | They propose to use Poincaré embeddings to improve existing approaches to domain-specific taxonomy induction from text as a signal for relocating wrong hyponym terms and attaching disconnected terms in a taxonomies. |
| Outcome: | The proposed method significantly improves state-of-the-art methods on the SemEval-2016 Task 13 on taxonomy extraction. |
Lifelong Model Editing with Graph-Based External Memory (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for post-training model editing suffer from overfitting and catastrophic forgetting. |
| Approach: | They propose a framework that leverages hyperbolic geometry and graph neural networks for precise and stable model edits. |
| Outcome: | Experiments on CounterFact, CounterFACT+, and MQuAKE with GPT2-XL and GPT-J show that HYPE significantly enhances edit stability, factual accuracy, and multi-hop reasoning. |