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.
Our tutorial will be held on August 8, 10 am - 1 pm (PDT). Slides may be subject to updates.
Time | Section | Presenter |
---|---|---|
10:00—10:15 | Section 1: Introduction - What are text-editing models? [Slides] | Eric |
10:15—10:50 | Section 2: Model Design [Slides] | Eric, Jonathan |
10:50—11:25 | Section 3: Applications [Slides] | Yue |
11:25—11:30 | Break | |
11:30—11:45 | Section 4: Controllable Generation [Slides] | Yue |
11:45—11:55 | Section 5: Multilingual Text Editing [Slides] | Eric |
11:55—12:25 | Section 6: Faster (Large) Language Models [Slides] | Jonathan |
12:25—12:30 | Section 7: Recommendations & Future Directions [Slides] | Eric |
12:30—13:00 | Q & A Session |
@inproceedings{kdd2023-text-editing-tutorial,
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},
title = {Fast Text Generation with Text-Editing Models},
year = {2023},
isbn = {9798400701030},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3580305.3599579},
doi = {10.1145/3580305.3599579},
booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {5815–5816},
location = {Long Beach, CA, USA},
series = {KDD '23}
}