Papers by Yingzhe Guo
stopes - Modular Machine Translation Pipelines (2022.emnlp-demos)
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Pierre Andrews, Guillaume Wenzek, Kevin Heffernan, Onur Çelebi, Anna Sun, Ammar Kamran, Yingzhe Guo, Alexandre Mourachko, Holger Schwenk, Angela Fan
| Challenge: | Neural machine translation is a natural language deep learning application that needs data to be trained. |
| Approach: | They describe a framework that empowers scalability and versatility for research use cases. |
| Outcome: | The proposed framework empowers scalability and versatility for research use cases. |