@inproceedings{malmi-etal-2022-text,
    title = "Text Generation with Text-Editing Models",
    author = "Malmi, Eric  and
      Dong, Yue  and
      Mallinson, Jonathan  and
      Chuklin, Aleksandr  and
      Adamek, Jakub  and
      Mirylenka, Daniil  and
      Stahlberg, Felix  and
      Krause, Sebastian  and
      Kumar, Shankar  and
      Severyn, Aliaksei",
    editor = "Ballesteros, Miguel  and
      Tsvetkov, Yulia  and
      Alm, Cecilia O.",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-tutorials.1",
    doi = "10.18653/v1/2022.naacl-tutorials.1",
    pages = "1--7",
    abstract = "Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, text 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 the text-edit based models and current state-of-the-art approaches analyzing their pros and cons. We discuss challenges related to deployment and how these models help to mitigate hallucination and bias, both pressing challenges in the field of text generation.",
}