# Text Morphing

arXiv: https://arxiv.org/abs/1810.00341 · HTML: https://mlsanity.com/paper/1810.00341/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2018.

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

- [Learning to Model Editing Processes](https://mlsanity.com/paper/2205.12374/) (2022)
- [MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing](https://mlsanity.com/paper/2502.09567/) (2025)

## Abstract (arXiv)

In this paper, we introduce a novel natural language generation task, termed as text morphing, which targets at generating the intermediate sentences that are fluency and smooth with the two input sentences. We propose the Morphing Networks consisting of the editing vector generation networks and the sentence editing networks which are trained jointly. Specifically, the editing vectors are generated with a recurrent neural networks model from the lexical gap between the source sentence and the target sentence. Then the sentence editing networks iteratively generate new sentences with the current editing vector and the sentence generated in the previous step. We conduct experiments with 10 million text morphing sequences which are extracted from the Yelp review dataset. Experiment results show that the proposed method outperforms baselines on the text morphing task. We also discuss directions and opportunities for future research of text morphing.