# Sentence Embeddings in NLI with Iterative Refinement Encoders

arXiv: https://arxiv.org/abs/1808.08762 · HTML: https://mlsanity.com/paper/1808.08762/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph](https://mlsanity.com/paper/2111.10541/) (2021)
- [BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matching](https://mlsanity.com/paper/2111.02188/) (2021)
- [Sentence Embeddings by Ensemble Distillation](https://mlsanity.com/paper/2104.06719/) (2021)
- [Robust Embeddings Via Distributions](https://mlsanity.com/paper/2104.08420/) (2021)
- [Chinese Sentences Similarity via Cross-Attention Based Siamese Network](https://mlsanity.com/paper/2104.08787/) (2021)
- [Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency](https://mlsanity.com/paper/2205.01759/) (2022)
- [Persian Natural Language Inference: A Meta-learning approach](https://mlsanity.com/paper/2205.08755/) (2022)

## Abstract (arXiv)

Sentence-level representations are necessary for various NLP tasks. Recurrent neural networks have proven to be very effective in learning distributed representations and can be trained efficiently on natural language inference tasks. We build on top of one such model and propose a hierarchy of BiLSTM and max pooling layers that implements an iterative refinement strategy and yields state of the art results on the SciTail dataset as well as strong results for SNLI and MultiNLI. We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks. Furthermore, our model beats the InferSent model in 8 out of 10 recently published SentEval probing tasks designed to evaluate sentence embeddings' ability to capture some of the important linguistic properties of sentences.