8 citations · 16 across the 7 of their papers we have counts for
10 papers
DiffusER: Discrete Diffusion via Edit-based Reconstruction
Machel Reid, Vincent J. Hellendoorn, Graham Neubig
In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revis…
M2D2: A Massively Multi-domain Language Modeling Dataset
Machel Reid, Victor Zhong, Suchin Gururangan +1
We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs). M2D2 consists of 8.5B tokens and spans 145 domains extracted…
Learning to Model Editing Processes
Machel Reid, Graham Neubig
Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating…
AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages
Machel Reid, Junjie Hu, Graham Neubig +1
Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource…
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining
Machel Reid, Mikel Artetxe
Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora, and do not make use of the strong cross-lingual signal c…
LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
Machel Reid, Victor Zhong
Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from I had a terrible time... to I had a great time...). We propose a coar…