Papers by Nathan Ng
fairseq: A Fast, Extensible Toolkit for Sequence Modeling (N19-4)
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Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli
| Challenge: | OpenNMT is a community-built toolkit written in multiple languages with an emphasis on extensibility. |
| Approach: | They propose to use PyTorch to train custom sequence models for translation, summarization, language modeling, and other tasks. |
| Outcome: | The proposed toolkit is fast, extensible, and useful for both research and production. |
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness (2020.emnlp-main)
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| Challenge: | Data augmentation is a common method used to improve out-of-domain (OOD) generalization. |
| Approach: | They propose a data augmentation method that uses corruption and reconstruction functions to move randomly on a manifold to generate training examples. |
| Outcome: | The proposed method outperforms existing methods and baseline models on both in-domain and OOD data and achieves gains of 0.8% on OOD Amazon reviews, 1.8% accuracy on OOO MNLI, and 1.4 BLEU on in- domain IWSLT14 German-English. |