CMU Artificial Intelligence Seminar Series sponsored by
Friday, May 17, 2022
Time: 12:00 - 01:00 PM ET
Machel Reid -- Incorporating Text Editing in Natural Language Processing
Relevant Paper(s):
- Variational Inference for Learning Representations of Natural Language Edits
- LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
- Learning to Model Editing Processes
Abstract:
Most current text generation applications in NLP use fully autoregressive modelling, a notable example of this being the models in the GPT series. However, most original content (e.g. art, books, articles, source code) is developed not in a single iteration, but in many iterations with each more refined than the last. In other words, revising and editing are a central part of the human creative workflow. Given this, in this talk I will talk about ideas for trying to bridge this disconnect by incorporating text editing into standard natural language processing as well as my work associated with various aspects of editing.
Bio: Machel Reid is a researcher at the University of Tokyo working on natural language processing. He has worked on multilinguality, primarily focusing on low-resource languages and multilinguality pre-training. He is currently under the supervision of Yutaka Matsuo, and has been an intern at Carnegie Mellon University under the supervision of Graham Neubig. He is also an incoming PhD Student at the University of Washington advised by Luke Zettlemoyer and Noah Smith.