Text Generation with Text-Editing Models

TL;DR

Text-editing models are a promising alternative to seq2seq models for monolingual text generation tasks, offering faster inference, better efficiency, and improved control, with applications in error correction, simplification, and style transfer.

Contribution

This paper provides a comprehensive overview of text-editing models, analyzing their advantages, challenges, and potential for mitigating hallucination and bias in text generation.

Findings

Text-editing models outperform seq2seq models in inference speed and efficiency.

They offer better control and interpretability of generated outputs.

Challenges include productionization and addressing hallucination and bias.

Abstract

Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer. These tasks share a common trait - they exhibit a large amount of textual overlap between the source and target texts. Text-editing models take advantage of this observation and learn to generate the output by predicting edit operations applied to the source sequence. In contrast, seq2seq models generate outputs word-by-word from scratch thus making them slow at inference time. Text-editing models provide several benefits over seq2seq models including faster inference speed, higher sample efficiency, and better control and interpretability of the outputs. This tutorial provides a comprehensive overview of text-editing models and current state-of-the-art approaches, and analyzes their pros and cons. We discuss challenges related to productionization and how these models can be used to mitigate hallucination and bias, both pressing challenges in the field of text generation.