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Questions and answers categorized in: Artificial Intelligence > EITC/AI/GCML Google Cloud Machine Learning > Further steps in Machine Learning > Natural language generation > Examination review

How can RNNs learn to pay attention to specific pieces of structured data during the generation process?

Wednesday, 02 August 2023 by EITCA Academy

Recurrent Neural Networks (RNNs) have been widely used in Natural Language Generation (NLG) tasks, where they generate human-like text based on given input data. In some cases, it is desirable for RNNs to learn to pay attention to specific pieces of structured data during the generation process. This ability allows the model to focus on

  • Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Natural language generation, Examination review
Tagged under: Artificial Intelligence, Attention Mechanism, Machine Learning, Natural Language Generation, Recurrent Neural Networks

What are the advantages of using recurrent neural networks (RNNs) for natural language generation?

Wednesday, 02 August 2023 by EITCA Academy

Recurrent Neural Networks (RNNs) have gained significant attention and popularity in the field of Natural Language Generation (NLG) due to their unique advantages and capabilities. NLG is a subfield of Artificial Intelligence that focuses on generating human-like text based on input data. RNNs, a type of neural network architecture, have proven to be particularly effective

  • Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Natural language generation, Examination review
Tagged under: Ambiguity Handling, Artificial Intelligence, Backpropagation Through Time, Contextual Understanding, Natural Language Generation, NLG, RNNs, Sequential Processing, Variable-Length Input

What are the limitations of using a template-based approach for natural language generation?

Wednesday, 02 August 2023 by EITCA Academy

A template-based approach is one of the commonly used methods for natural language generation (NLG). This approach involves creating predefined templates that can be filled with specific data to generate human-like text. While template-based NLG has its advantages, it also comes with several limitations that need to be considered. One limitation of using a template-based

  • Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Natural language generation, Examination review
Tagged under: Adaptability, Artificial Intelligence, Creativity, Flexibility, Grammatical Variations, Limitations, Linguistic Structures, Maintenance, Natural Language Generation, NLG, Template-based Approach

How does machine learning enable natural language generation?

Wednesday, 02 August 2023 by EITCA Academy

Machine learning plays a important role in enabling natural language generation (NLG) by providing the necessary tools and techniques to process and understand human language. NLG is a subfield of artificial intelligence (AI) that focuses on generating human-like text or speech based on given input or data. It involves transforming structured data into coherent and

  • Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Natural language generation, Examination review
Tagged under: Artificial Intelligence, Attention Mechanisms, Long Short-Term Memory Networks, Machine Learning, Natural Language Generation, Pre-trained Language Models, Recurrent Neural Networks, Supervised Learning, Unsupervised Learning

What is the role of structured data in natural language generation?

Wednesday, 02 August 2023 by EITCA Academy

Structured data plays a important role in natural language generation (NLG) within the field of artificial intelligence. NLG refers to the process of generating human-like text or speech from structured data, enabling machines to communicate effectively with humans. Structured data, in this context, refers to data that is organized and formatted in a way that

  • Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Natural language generation, Examination review
Tagged under: Artificial Intelligence, Data Formatting, Data Organization, NLG, Structured Data
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