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Found 4 papers, 2 papers with code
Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in NLU
However, given the trend of larger pre-trained models, fine-tuning these models for each downstream task is parameter-inefficient and computationally-expensive deeming this approach sub-optimal for adoption by NLU systems.
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.
Ranked #14 on
Natural Language Inference
on CommitmentBank
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
Instilling Type Knowledge in Language Models via Multi-Task QA
Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types.
