# Learning Generic Sentence Representations Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1611.07897 · HTML: https://mlsanity.com/paper/1611.07897/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2016.

## Built on by (most cited first, 5 of 5)

- [Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence](https://mlsanity.com/paper/2305.03010/) (2023)
- [Paraphrastic Representations at Scale](https://mlsanity.com/paper/2104.15114/) (2021)
- [Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases](https://mlsanity.com/paper/2207.12759/) (2022)
- [Unsupervised Document Embedding via Contrastive Augmentation](https://mlsanity.com/paper/2103.14542/) (2021)
- [Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation](https://mlsanity.com/paper/2210.09773/) (2022)

## Abstract (arXiv)

We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoder-decoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.