[Figure: Table 4 comparing Transformer performance on English constituency parsing against other models]
Caption: Table 4: The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)
Figure text: Pars…For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new …
Minh-Thang Luong
scientist · 2 mentions across 1 reading
In this course
Minh-Thang Luong is a machine learning researcher known for work on sequence-to-sequence models and neural machine translation, appearing in the course materials as part of the foundational research on Transformer architecture and its applications to translation tasks. The excerpts position his contributions within the context of how modern neural architectures achieve state-of-the-art performance on machine translation benchmarks, illustrating the shift from recurrent to attention-based models that became central to contemporary AI systems. His presence in these readings anchors the technical genealogy of transformer models that now underpin large language models and multimodal AI systems examined throughout the seminar.
Mentioned in 1 reading
Appears alongside
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