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Found 6 papers, 1 papers with code
Design Considerations For Hypothesis Rejection Modules In Spoken Language Understanding Systems
Hypothesis rejection modules in both schemes reject/accept a hypothesis based on features drawn from the utterance directed to the SLU system, the associated SLU hypothesis and SLU confidence score.
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
One-vs-All Models for Asynchronous Training: An Empirical Analysis
An OVA system consists of as many OVA models as the number of classes, providing the advantage of asynchrony, where each OVA model can be re-trained independent of other models.
A Re-ranker Scheme for Integrating Large Scale NLU models
An ideal re-ranker will exhibit the following two properties: (a) it should prefer the most relevant hypothesis for the given input as the top hypothesis and, (b) the interpretation scores corresponding to each hypothesis produced by the re-ranker should be calibrated.
Play Duration based User-Entity Affinity Modeling in Spoken Dialog System
We first binarize play durations to obtain implicit positive and negative affinity labels.
