36 citations · 59 across the 12 of their papers we have counts for
15 papers
Domain adapted machine translation: What does catastrophic forgetting forget and why?
Danielle Saunders, Steve DeNeefe
Neural Machine Translation (NMT) models can be specialized by domain adaptation, often involving fine-tuning on a dataset of interest. This process risks catastrophic forgetting: r…
Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation
Danielle Saunders, Katrina Olsen
The vast majority of work on gender in MT focuses on 'unambiguous' inputs, where gender markers in the source language are expected to be resolved in the output. Conversely, this p…
Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey
Danielle Saunders
The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. Howe…
Inference-only sub-character decomposition improves translation of unseen logographic characters
Danielle Saunders, Weston Feely, Bill Byrne
Neural Machine Translation (NMT) on logographic source languages struggles when translating `unseen' characters, which never appear in the training data. One possible approach to t…
Addressing Exposure Bias With Document Minimum Risk Training: Cambridge at the WMT20 Biomedical Translation Task
Danielle Saunders, Bill Byrne
The 2020 WMT Biomedical translation task evaluated Medline abstract translations. This is a small-domain translation task, meaning limited relevant training data with very distinct…
Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It
Danielle Saunders, Rosie Sallis, Bill Byrne
Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many ex…