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Found 5 papers, 0 papers with code
Building Adaptive Acceptability Classifiers for Neural NLG
We propose a novel framework to train models to classify acceptability of responses generated by natural language generation (NLG) models, improving upon existing sentence transformation and model-based approaches.
Getting to Production with Few-shot Natural Language Generation Models
In this paper, we study the utilization of pre-trained language models to enable few-shotNatural Language Generation (NLG) in task-oriented dialog systems.
Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task
Converting a knowledge graph or sub-graph to natural text is useful when answering questions based on a knowledge base.
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data
In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production.
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models
Label noise is increasingly prevalent in datasets acquired from noisy channels.
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Image Classification
on Clothing1M
(using extra training data)
