Human language technologies
Advancing how machines understand and generate human language across speech, text, and translation.
LiST (Lite Self-Training)
We present a new method LiST for efficient fine-tuning of large pre-trained language models (PLMs) in few-shot learning settings. LiST significantly improves over recent methods that adopt prompt fine-tuning using two key techniques. The first…
LITMUS Predictor
LITMUS Predictor provides support for simulating performance in ~100 languages given training observations of the desired task-model. Each training observation specifies the finetuning-datasize + test-performance in different languages. Further, the tool provides support for constructing…
Stochastic Mixture-of-Experts
This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts.
Deep Neural Machine Translation
This PyTorch package implements Very Deep Transformers for Neural Machine Translation, to stabilize the large scale language model and neural machine translation training, as described in: Very deep transformers for neural machine translation
Meta Self-training for Few-shot Neural Sequence Labeling [Code]
This is the implementation of the paper Meta Self-training for Few-shot Neural Sequence Labeling. MetaST is short for meta-learning for self-training.
Microsoft Translator: Now translating 100 languages and counting!
Today, we’re excited to announce that Microsoft Translator has added 12 new languages and dialects to the growing repertoire of Microsoft Azure Cognitive Services Translator, bringing us to a total of 103 languages! The new…
