What are the limitations of using a template-based approach for natural language generation?
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 approach is the lack of flexibility and adaptability. Templates are designed for specific scenarios and may not cover all possible variations or edge cases. For instance, if a template is created to generate product descriptions, it may not be able to handle new product features or attributes that were not considered during the template creation. This limitation can lead to repetitive and rigid output, which may not be suitable for generating diverse and dynamic content.
Another limitation is the inability to handle complex linguistic structures and grammatical variations. Templates are typically designed to cover a limited set of sentence structures and may not be able to handle more intricate language patterns. For example, if a template is created to generate sentences in the active voice, it may struggle to generate passive voice sentences or handle complex sentence constructions. This limitation can result in unnatural or grammatically incorrect output.
Additionally, template-based NLG may lack the ability to generate truly creative and original content. Since templates rely on predefined structures and data placeholders, the generated text tends to be predictable and formulaic. This limitation becomes more apparent when generating content that requires a high level of creativity, such as poetry or storytelling. Template-based NLG may struggle to produce unique and engaging narratives that capture the reader's attention.
Moreover, maintaining and updating templates can be a time-consuming and resource-intensive task. As the underlying data or requirements change, templates need to be modified or expanded to accommodate these changes. This process can be cumbersome, especially when dealing with a large number of templates or frequent updates. Additionally, template-based NLG may require expertise in both the domain knowledge and the NLG system itself, which can further increase the complexity and cost of maintenance.
While template-based NLG can be a useful approach for generating certain types of content, it has limitations in terms of flexibility, linguistic complexity, creativity, and maintenance. These limitations should be taken into consideration when deciding on the appropriate NLG approach for a given task.
Other recent questions and answers regarding EITC/AI/GCML Google Cloud Machine Learning:
- What's a support vector machine
- How difficult is it for a beginner to make a model that can help in the search for asteroids?
- Would machine learning be able to overcome bias?
- What is regularization?
- Is there a type of training an AI model in which both the supervised and unsupervised learning approaches are implemented at the same time?
- How is learning occurring in unsupervised machine learning systems?
- How to use Fashion-MNIST dataset in Google Cloud Machine Learning / AI Platform?
- What types of algorithms for machine learning are there and how does one select them?
- When a kernel is forked with data and the original is private, can the forked one be public and if so is not a privacy breach?
- Can NLG model logic be used for purposes other than NLG, such as trading forecasting?
View more questions and answers in EITC/AI/GCML Google Cloud Machine Learning