# Hyperbolic sentence representations for solving Textual Entailment

arXiv: https://arxiv.org/abs/2406.15472 · HTML: https://mlsanity.com/paper/2406.15472/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Order-Embeddings of Images and Language](https://mlsanity.com/paper/1511.06361/) (2015)
- [Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features](https://mlsanity.com/paper/1703.02507/) (2017)
- [Reasoning about Entailment with Neural Attention](https://mlsanity.com/paper/1509.06664/) (2015)
- [Learning Natural Language Inference with LSTM](https://mlsanity.com/paper/1512.08849/) (2015)
- [Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention](https://mlsanity.com/paper/1605.09090/) (2016)
- [Siamese CBOW: Optimizing Word Embeddings for Sentence Representations](https://mlsanity.com/paper/1606.04640/) (2016)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Mamba Meets Hyperbolic Geometry: A New Paradigm for Structured Language Embeddings](https://mlsanity.com/paper/2505.18973/) (2025)
- [Hyperbolic Large Language Models](https://mlsanity.com/paper/2509.05757/) (2025)

## Abstract (arXiv)

Hyperbolic spaces have proven to be suitable for modeling data of hierarchical nature. As such we use the Poincare ball to embed sentences with the goal of proving how hyperbolic spaces can be used for solving Textual Entailment. To this end, apart from the standard datasets used for evaluating textual entailment, we developed two additional datasets. We evaluate against baselines of various backgrounds, including LSTMs, Order Embeddings and Euclidean Averaging, which comes as a natural counterpart to representing sentences into the Euclidean space. We consistently outperform the baselines on the SICK dataset and are second only to Order Embeddings on the SNLI dataset, for the binary classification version of the entailment task.