293 citations · 603 across the 10 of their papers we have counts for
4 papers · 1 filter
Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg +5
In this work, we study the effect of varying the architecture and training data quality on the data scaling properties of Neural Machine Translation (NMT). First, we establish that…
Scaling Laws for Neural Machine Translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag +5
We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
Efficient Sequence Labeling with Actor-Critic Training
Saeed Najafi, Colin Cherry, Grzegorz Kondrak
Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same pu…