163 citations · 173 across the 3 of their papers we have counts for
5 papers
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi
Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve…
Simple and Effective Noisy Channel Modeling for Neural Machine Translation
Kyra Yee, Nathan Ng, Yann N. Dauphin +1
Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence. This makes decoding decisions based on pa…
Facebook FAIR's WMT19 News Translation Task Submission
Nathan Ng, Kyra Yee, Alexei Baevski +3
This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two language pairs and four language directions, English <-> German and…
Embryo staging with weakly-supervised region selection and dynamically-decoded predictions
Tingfung Lau, Nathan Ng, Julian Gingold +3
To optimize clinical outcomes, fertility clinics must strategically select which embryos to transfer. Common selection heuristics are formulas expressed in terms of the durations r…
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott, Sergey Edunov, Alexei Baevski +5
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text…