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Found 6 papers, 1 papers with code
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
+2
Differentially Private Decoding in Large Language Models
Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart.
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
Ranked #1 on
Slot Filling
on MASSIVE
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU
In this paper, we present our results on boosting NLU model performance through training data augmentation using a sequential generative adversarial network (GAN).
Using multiple ASR hypotheses to boost i18n NLU performance
For IC and NER multi-task experiments, when evaluating on the mismatched test set, we see improvements across all domains in German and in 17 out of 19 domains in Portuguese (improvements based on change in SeMER scores).
Abstractive Text Summarization
Automatic Speech Recognition
+5
Using Alternate Representations of Text for Natural Language Understanding
One of the core components of voice assistants is the Natural Language Understanding (NLU) model.
