A
Angela Fan
Researcher at Facebook
Publications - 85
Citations - 11665
Angela Fan is an academic researcher from Facebook. The author has contributed to research in topics: Computer science & Machine translation. The author has an hindex of 25, co-authored 72 publications receiving 7026 citations. Previous affiliations of Angela Fan include Stanford University & Harvard University.
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fairseq: A Fast, Extensible Toolkit for Sequence Modeling.
Myle Ott,Sergey Edunov,Alexei Baevski,Angela Fan,Sam Gross,Nathan Ng,David Grangier,Michael Auli +7 more
TL;DR: fairseq as discussed by the authors is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks, and supports distributed training across multiple GPUs and machines.
Proceedings ArticleDOI
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott,Sergey Edunov,Alexei Baevski,Angela Fan,Sam Gross,Nathan Ng,David Grangier,Michael Auli +7 more
TL;DR: 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 generation tasks and supports distributed training across multiple GPUs and machines.
Proceedings Article
Language modeling with gated convolutional networks
TL;DR: A finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens, is developed and is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks.
Posted Content
Language Modeling with Gated Convolutional Networks
TL;DR: The authors proposed a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens and achieved state-of-the-art results on the WikiText-103 benchmark.
Proceedings Article
Wizard of Wikipedia: Knowledge-Powered Conversational Agents
TL;DR: The best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while a new benchmark allows for measuring further improvements in this important research direction.