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Found 4 papers, 0 papers with code
Scalable Prompt Generation for Semi-supervised Learning with Language Models
In this paper, we propose two methods to automatically design multiple prompts and integrate automatic verbalizer in SSL settings without sacrificing performance.
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
Exploring Transfer Learning For End-to-End Spoken Language Understanding
Since our model can process both speech and text input sequences and learn to predict a target sequence, it also allows us to do zero-shot E2E SLU by training on only text-hypothesis data (without any speech) from a new domain.
Ranked #3 on
Spoken Language Understanding
on Snips-SmartLights
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Group Scissor: Scaling Neuromorphic Computing Design to Large Neural Networks
Following rank clipping, group connection deletion further reduces the routing area of LeNet and ConvNet to 8. 1\% and 52. 06\%, respectively.
