research
∙
04/18/2021
Demystifying the Better Performance of Position Encoding Variants for Transformer
Transformers are state of the art models in NLP that map a given input s...
share
research
∙
10/24/2020
Rethinking embedding coupling in pre-trained language models
We re-evaluate the standard practice of sharing weights between input an...
research
∙
08/15/2020
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition
Transformer-based models have achieved stateof-the-art results in many t...
research
∙
09/01/2019
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation
The recently proposed massively multilingual neural machine translation ...
research
∙
08/31/2019
