Machine Translation
eb011fd258c763c44d8c6a0e9ce04f17-Paper-Datasets_and_Benchmarks.pdf
In this work, we apply these ideas to sign language recognition.
Unsupervised Text Style Transfer using Language Models as Discriminators
Zichao Yang, Zhiting Hu, Chris Dyer, Eric P. Xing, Taylor Berg-Kirkpatrick
Neural Information Processing Systems http://nips.cc/
Learning to Scaffold: Optimizing Model Explanations for Teaching
Patrick Fernandes, Marcos Treviso, Danish Pruthi, André F. T. Martins, Graham Neubig
First, a student model is trained to recover the classifier'spredictions andtomatch theexplanations givenbytheexplainer.
Fast Structured Decoding for Sequence Models
Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhihong Deng
Neural Information Processing Systems http://nips.cc/
Novel positional encodings to enable tree-based transformers
Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-totree, tree-to-sequence, and tree-to-tree mappings. Our approach abstracts the transformer'ssinusoidal positional encodings, allowing ustoinstead useanovel positional encoding scheme to represent node positions within trees.
