Summary of Hyperbolic Sentence Representations For Solving Textual Entailment, by Igor Petrovski
Hyperbolic sentence representations for solving Textual Entailment
by Igor Petrovski
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper explores the application of hyperbolic spaces in solving Textual Entailment tasks. The authors employ the Poincare ball to embed sentences in a hierarchical manner. To evaluate their approach, they develop two new datasets and compare their results with various baselines, including LSTMs, Order Embeddings, and Euclidean Averaging. Their model consistently outperforms the baselines on the SICK dataset and achieves comparable performance on the SNLI dataset for binary classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at using special spaces called hyperbolic spaces to help computers understand relationships between sentences. They use a specific way of putting sentences into these spaces, called the Poincare ball, to show that this can be useful for tasks like Textual Entailment. To test their idea, they make some new datasets and compare how well it works against other ways of doing things. Their method does better than most others on one dataset and almost as well on another. |
Keywords
* Artificial intelligence * Classification
