# Sentiment Transfer using Seq2Seq Adversarial Autoencoders

arXiv: https://arxiv.org/abs/1804.04003 · HTML: https://mlsanity.com/paper/1804.04003/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [Political Depolarization of News Articles Using Attribute-aware Word Embeddings](https://mlsanity.com/paper/2101.01391/) (2021)
- [T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation](https://mlsanity.com/paper/2212.01667/) (2022)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Topical Language Generation using Transformers](https://mlsanity.com/paper/2103.06434/) (2021)

## Abstract (arXiv)

Expressing in language is subjective. Everyone has a different style of reading and writing, apparently it all boil downs to the way their mind understands things (in a specific format). Language style transfer is a way to preserve the meaning of a text and change the way it is expressed. Progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data, use cases, and reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose a model combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of evaluating style transfer tasks, we frame the problem as sentiment transfer and evaluation using a sentiment classifier to calculate how many sentiments was the model able to transfer. We report our results on several kinds of models.