# Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron

arXiv: https://arxiv.org/abs/1906.01942 · HTML: https://mlsanity.com/paper/1906.01942/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

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

- [Lightweight Cross-Lingual Sentence Representation Learning](https://mlsanity.com/paper/2105.13856/) (2021)
- [Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training](https://mlsanity.com/paper/2610.06161/) (2026)

## Abstract (arXiv)

We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoencoding and neural machine translation to force the source and target sentence embeddings to share the same space without the help of a pivot language or an additional transformation. We train a multilayer perceptron on top of the sentence embeddings to extract good bilingual sentence pairs from nonparallel or noisy parallel data. Our approach shows promising performance on sentence alignment recovery and the WMT 2018 parallel corpus filtering tasks with only a single model.